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ID: 59575
Title: Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes.
Author: Anirudha Ghosh, P.K. Joshi.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 76-93 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Land surface temperature (LST), Hyperspectral imagery, Sharpening spatial resolution, Support vector machine (SVM), Gradient boosting machine (GBM), Partial least square (PLS).
Abstract: Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLAST) derived from the Landsat ETM+ thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m , 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The result indicate that wavelengths centered around 457, 671, 1488 and 2013-2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.
Location: TE 12 New Biology Building
Literature cited 1: Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Neale, C.M.U., 2007a. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens. Environ. 107, 547-558. Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Colaizzi, P.D., 2007b. Utility of thermal sharpening over Texas high plains irrigated agricultural fields. J. Geophys.Res.112, D19110.http://dx.doi.org/10.1029/2007JD008407.
Literature cited 2: Agam, N., Kustas, W.P., Anderson, M.C., Li, F.Q., Colaizzi, P.D., 2008. Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions. Geophys.Res. Lett. 35, L02402. http://dx.doi.org/10.1029/2007GL032195. Anderson, M.C., Norman, J.M., Kustas, W.P., Houborg, R., Starks, P.J., Agam, N., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens. Environ. 112, 4227-4241.

ID: 59574
Title: Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.
Author: Mariana Belgiu, Lucian Dragut
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 67-75 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Supervised segmentation, Unsupervised segmentation, OBIA, Buildings, Random forest classifier, OpenStreetMap.
Abstract: Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ' optimal segmentation ' . Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.
Location: TE 12 New Biology Building
Literature cited 1: Anders, N.S., Seijmonsbergen, A.C., Bouten, W., 2011. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 115, 2976-2985. Arvor, D., Durieux, L., Andres, S., Laporte, M.-A., 2013. Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogrammetry Remote Sens. 82, 125-137.
Literature cited 2: Baatz, M., Schape, A., 2000. Multiresolution segmentation -an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G., (Eds). Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag.Karlsruhe, Germany.pp. 12-23. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensingdata for GIS-ready information.ISPRS J. Photogrammetry Remote Sens. 58, 239-258.

ID: 59573
Title: Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies.
Author: Jari Vauhkonen, Erik Naesset, Terje Gobakken.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 57-66 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Light Detection and Ranging (LiDAR), Forest inventory, Tree allometry, Delaunay triangulation, Alpha shape, Simplicial homomorphism, Persistent homology.
Abstract: A computational canopy volume (CCV) based on airborne laser scanning(ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500-1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88-0.89, 0.89, 0.83-0.97, and 0.88-0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77-0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0-1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.
Location: TE 12 New Biology Building
Literature cited 1: Anon, 1999. Pinnacle User ' s Manual. Javad Positioning Systems, San Jose, CA, p. 123. Beland, M., Baldocchi, D.D., Widlowski, J.-L., Fournier, R.A., Verstrete, M.M., 2014. On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrtial LiDAR. Agric. Forest Meteorol. 184, 82-97
Literature cited 2: Braastad, H., 1966. Volume tables for birch. Meddeleser fra Det norske Skogforsoksvesen 21, 265-365 (In Norwegian with an English summary). Brantseg, A., 1967. Volume functions and tables for scots pine. South Norway. Meddelelser fra Det norske Skogforsoksvesen 22, 689-739 (In Norwegian with an English summary).

ID: 59572
Title: Automatic 3D modeling of metal frame connections from LiDAR data for structural engineering purposes
Author: M. Cabaleiro, B. Riveiro, P.Arias, J.C. Caamano, J.A. Vilan
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 47-56 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Automatic 3D modelling, LiDAR, Point cloud, Structural analysis, Metal frame.
Abstract: The automatic generation of 3D as-built models from LiDAR data is a topic where significant progress has been made in recent years. This paper describes a new method for the detection and automatic 3D modelling of frame connections and the formation of profiles comprising a metal frame from LiDAR data. The method has been developed using an approach to create 2.5 D density images for subsequent processing using the Hough transform. The structure connections can be automatically identified after selecting areas in the point cloud. As a result, the coordinates of the connection centre composition (profiles, size and shape of the haunch) and direction of their profiles are extracted. A standard file is generated with the data obtained from the geometric and semantic characterization of the connections. The 3D model of connections and metal frames, which are suitable for processing software for structural engineering applications, are generated automatically based on this file. The algorithm presented in this paper has been tested under laboratory conditions and also with several industrial portal frames, achieving promising results. Finally, 3D models were generated, and structural calculations were performed.
Location: TE 12 New Biology Building
Literature cited 1: Bosche, F., 2010. Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv. Eng. Inform. 24 (1), 107-118. Bosche, F., 2011. Plane-based registration of construction laser scans with 3D/ 4D building models. Adv. Eng. Inform. 26 (1), 90-102.
Literature cited 2: Bosche, F., Haas, C.T., 2008. Automated retrieval of 3D CAD model objects in construction range images. Automat. Constr. 17 (4), 499-512 Eurocode-3: design of steel structures-Part1-1: general rules and rules for building (EN 1993-1-1:2005), 2005. European Committee for standardization, Brussels.

ID: 59571
Title: Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data.
Author: Xianfeng Jiao, John M. Kovacs, Jiali Shang, Heather McNairn, Dan Walters, Baoluo Ma, Xiaoyuan Geng.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 38-46 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Crops, Object-oriented classification, polSAR, Polarimetric decomposition, Crop mapping, Phenological monitoring.
Abstract: The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT -2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the clode-Pottier and Freeman-Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95 % and Kappa of 0.93; a 6 % improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude-Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman-Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phonological growth stage based on the BBCH scale, and the HV backscatter and entropy.
Location: TE 12 New Biology Building
Literature cited 1: Alberga, V., 2007. A study of land cover classification using polarimetric SAR parameters. Int. J. Remote Sens. 28 (17), 3851-3870. Alberga, V., Satalino, G., Staykova, D.K., 2008. Comparison of polarimetric SAR observables in terms of classification performance.Int. J. Remote Sens. 29 (14), 4129-4150
Literature cited 2: Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58 (3-4), 239-258. Blaes, X., Vanhalle, L., Defourny, P., 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sens. Environ. 96 (3), 352-365.

ID: 59570
Title: An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections.
Author: Alberto Holgado-Barco, Diego Gonzalez-Aguilera, Pedro Arias-Sanchez, Joaquin Martinez-Sanchez.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 28-37 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Mobile mapping system, Mobile LiDAR system, Geospatial information, Laser surveying, Road maintenance, Road inventories, Vertical alignment, Segmentation, Filtering, Parameterisation, Feature extraction.
Abstract: The characterisation the vertical profiles and cross-sections of roads is important for the verification of proper construction and road safety assessment. The goal of this paper is the extraction of geometric parameters through the automatic processing of mobile LiDAR system (MLS) point clouds. Massive and complex datasets provided by the MLS are processed using a hierarchical strategy that includes segmentation, principal component analysis (PCA)-based orthogonal regression, filtering and parameter extraction procedures. Best-fit geometric parameters act as a vertical road model for both linear parameters (slope and vertical curves) and cross-sections (superrelevations). The proposed automatic processing approach gives satisfactory results for the analysed scenario.
Location: TE 12 New Biology Building
Literature cited 1: Ai, C., Tsai, Y.J., 2012. Critical assessment of automatic traffic sign detection using three-dimensional LiDAR point cloud data. In: Transportation Research Board 91st Annual Meeting, pp 12-3214. Belton, D., Lichti, D.D., 2006. Classification and Segmentation of Terrestrial Laser S canner Pont C louds using Local Variance Information. IAPRS, XXXVI, 5
Literature cited 2: Cleveland, W.S., 1979. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74 (368), 839- 836. Cleveland, W.S., Devlin, S.J., 1988. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 74 (368), 829-836.

ID: 59569
Title: Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Author: Lei Ji, Li Zhang, Jennifer Rover, Bruce K. Wylie, Xuexia Chen.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 20-27 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Geostatistics, Nugget variance, Semivariogram, Signal-to-noise ratio, Spectral vegetation index, Standardized noise.
Abstract: In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
Location: TE 12 New Biology Building
Literature cited 1: Asmat, A., Atkinson, P.M., Foody, G.M., 2010. Geostatistically estimated image noise is a function of variance in the underlying signal. Int. J. Remote Sens. 31 (4), 1009-1025. Atkinson, P.M., 1997. On estimating measurement error in remotely sensed images with the variogram. Int. J. Remote Sens. 18 (14), 3075-3084.
Literature cited 2: Atkinson, P.M., Dunn, R., Harrison, A.R., 1996. Measurement error in reflectance data and its implications for regularizing the variogram. Int. J. Remote Sens. 17 (18), 3735-3750. Atkinson, P.M., Sargent, I.M., Foody, G.M., Williams, J., 2005. Interpreting image based methods for estimating the signal-to-noise ratio. Int. J. Remote Sens. 26 (22), 5099-5115.

ID: 59568
Title: An improved geopositioning model of QuickBird high resolution satellite imagery by compensating spatial correlated errors
Author: Chuang Li, Yunzhong Shen, Bofeng Li, Gang Qiao, Shijie Liu, Weian Wang, Xiaohua Tong.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 12-19 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: QuickBird imagery, Geopositioning, Spatial correlated errors, Least squares collocation, Variance component estimation, Rational function model.
Abstract: A lot of studies have been done for correcting the synthetic biases of high resolution satellite images (HRSI), which is a fundamental work in the geometric orientation and the geopositioning of HRSI. All the existing bias-corrected models eliminate the biases in the images by expressing the biases as a function of some deterministic parameters (i.e. shift, drift, or affine transformation models), which is indeed effective for most of the commercial high resolution satellite imagery (i.e. IKONOS, GeoEye-1, WorldView-1/2) except for QuickBird. Studies found that QuickBird is the only one that needs more than a simple shift model to absorb the strong residual systematic errors. To further improve the image geopositioning of QuickBird image, in this paper, we introduce space correlated errors (SCEs) and model them as signals in the bias-corrected rational function model (RFM) and estimate the SCEs at the ground control points (GCPs) together with the bias-corrected parameters using least squares collocation. With these estimated SCEs at GCPs. Finally, we carry out geopositioning for these unknown points after compensating both the biases and the SCEs. The performance of our improved geopositioning model is demonstrated with a stereo pair of QuickBird cross-track images in the Shanghai urban area. The results show that the SCEs exist in HRSI and the presented geopositioning model exhibits a significant improvement, larger than 20% in both latitude and height directions and about 2.8 % in longitude direction, in geopositioning accuracy compared to the common used affine transformation model (ATM), which is not taking SCEs into account. The statistical results also show that our improved geopositioning model is superior to the ATM and the second polynomial model (SPM) in both accuracy and reliability for the geopositioning.
Location: TE 12 New Biology Building
Literature cited 1: Aguilar, M.A., Aguilar, F.J., Aguera, F., Sanchez, J.A., 2007. Geometric accuracy assessment of QuickBird basic imagery using different operational approaches. Photogram.Eng.Remote Sens. 73 (12), 1321-1332. Aguilar, M.A., Aguera, F., Aguilar, F.J., et al., 2008. Geometric accuracy assessment of the orthorectification process from very high resolution satellite imagery for common agricultural policy purposes. Int.J.Remote Sens. 29 (24), 7181-7197.
Literature cited 2: Aguilar, M.A., Saldana, M.M., Aguilar, F.J., 2013. Assessing geometric accuracy of the orthorectification process from GeoEye-1 and WorldView-2 panchromatic images. Int. J. Appl. Earth Obs. Geoinf. 21, 427-435. Dial, G., Grodecki, J., 2002. Block adjustment with rational polynomial camera models. In: Proceedings of APSRS 2002 Annual Conference, Washington DC, 22-26 April (on CDROM).

ID: 59567
Title: Accurate mapping of forest types dense seasonal Landsat time-series.
Author: Xiaolin Zhu, Desheng Liu.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 96. 1-11 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Forest types, Classification, Landsat, Seasonal time-series, Hierarchical approach, Feature selection.
Abstract: An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g. Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52 % using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63 %. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phonological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales.
Location: TE 12 New Biology Building
Literature cited 1: Asner, G.P., Jones, M.O., Martin, R.E., Knapp, D.E., Hughes, R.F., 2008. Remote sensing of native and invasive species in Hawaiian forests. Remote Sens. Environ. 112 (5) 1912-1926 Band, L.E., 1993. Effect of land-surface representation on forest water and carbon budgets. J. Hydrol. 150 (2-4), 749-772.
Literature cited 2: Bazi, Y., Melgani, F., 2006. Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE T. Geosci. Remote 44, 3374-3385. Bolstad, P.V., Lillesand, T.M., 1992. Improved classification of forest vegetation in northern Wisconsin through a rule-based combination of soils, terrain, and Landsat Thematic Mapper data. For.Sci. 38 (1), 5-20.

ID: 59566
Title: Derivation of an urban materials spectral library through emittance and reflectance spectroscopy.
Author: Simone Kotthaus, Thomas E.L. Smith, Martin J. Wooster, C.S.B. Grimmond
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 194-212 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Spectral library, Impervious materials, FTIR spectroscopy, Reflectance, Emissivity.
Abstract: Recent advances in thermal infrared remote sensing include the increased availability of airborne hyper spectral imagers (such as the Hyperspectral Thermal Emission Spectrometer, HyTes, or the Telops HyperCam and the Specim aisaOWL), and it is planned that an increased number spectral bands in the long-wave infrared (LWIR) region will soon be measured from space at reasonably high spatial resolution (by imagers such as HyspIRI). Detailed LWIR emissivity spectra are required to best interpret the observations from such systems. This includes the highly heterogeneous urban environment, whose construction materials are not yet particularly well represented in spectral libraries. Here, we present a new online spectral library of urban construction materials including LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform Infrared (FTIR) spectrometer. FTIR emissivity measurements need to be carefully made; else they are prone to a series of errors relating to instrumental setup and radiometric calibration, which here relies on external blackbody sources. The performance of the laboratory-based emissivity measurement approach applied here, that in future can also be deployed in the field (e.g. to examine urban materials in situ), is evaluated herein. Our spectral library also contains matching short-wave (VIS-SWIR) reflectance spectra observed for each urban sample. This allows us to examine which characteristic (LWIR and) spectral signatures may in future best allow for the identification and discrimination of the various urban construction materials, that often overlap with respect to their chemical/mineralogical constituents. Hyper spectral or even strongly multi-spectral LWIR information appears especially useful, given that many urban materials are composed of minerals exhibiting notable reststrahlen/absorption effects in this spectral region. The final spectra and interpretations are included in the London Urban Micromet data archive (LUMA; http: //Londanclimate.info/LUMA/SLUM.html.).
Location: TE 12 New Biology Building
Literature cited 1: Abrams, M.J., Hook, S.J., 2013. NASA ' s hyper spectral infrared imager (HyspIRI). In: kuenzer, C., Dech, S. (Eds), Thermal Infrared Remote Sensing. Springer, Netherlands, pp.117-130 Adam, E., Mutanga, O., Rugege, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review .Wetl.Ecol.Manag.18, 281-296.
Literature cited 2: Akbari, H., Menon, S., Rosenfeld, A., 2009.Global cooling: increasing world-wide urban albedos to offset CO2.Clim.Change 94,275-286. Baldridge, A.M., Hook, S.J., Grove, C.I., Rivera, G., 2009.The ASTER spectral library version 2.0.Remote Sens. Environ.113, 711-715. http://dx.doi.org/10.1016/j.rse.2008.11.007.

ID: 59565
Title: A global optimization approach to roof segmentation from airborne lidar point clouds.
Author: Jixing Yan, Jie Shan, Wanshou Jiang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 183-193 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Segmentation, city modeling Buildings, Lidar, Point clouds, Global optimization
Abstract: This paper presents a global plane fitting approach for roof segmentation from lidar point clouds. Starting with conventional plane fitting approach (e.g., plane fitting based on region growing), an initial segmentation is first derived from roof lidar points. Such initial segmentation is then optimized by minimizing a global energy function consisting of the distances of lidar points to initial planes (labels), spatial smoothness between data points, and the number of planes. As global solution, the proposed approach can determine multiple roof planes simultaneously. Two lidar data sets of Indianapolis (USA) and Vaihingen (Germany) are used in the study. Experimental results show that the completeness and correctness are increased from 80.1% to 92.3% and 93.0% to 100% respectively; and the detection cross-lap rate reference cross-lap rate reduced from 11.9% to 2.2% and 24.6% to 5.8%, respectively. As a result, the incorrect segmentation that often occurs at plane transitions is satisfactorily resolved; and the topological consistency among segmented planes is correctly retained even for complex roof structures.
Location: TE 12 New Biology Building
Literature cited 1: Awrangjeb, M., Ravanbakhsh, M., Fraser, C.S., 2010. Automatic detection of residential buildings using LIDAR data and multispectral Imagery. ISPRS J. Photogram. Rem. Sens.65 (5), 457-467. Awwad, T.M., Zhu, Q., DU, Z., Zhang, Y., 2010. An improved segmentation approach for planar surfaces from unstructured 3D point clouds. Photogram. Rec.25 (1290, 5-23.
Literature cited 2: Boykov, Y., Veksler, O., Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach.Intell.23 (11), 1222-1239. Bretar, F., Roux, M., 2005.Extraction of 3D planar primitives from raw airborne laser data: a normal driven RANSAC approach. In: proceedings of IAPR conference on Machine Vision Applications, Tsukuba, Japan, pp.452-455.

ID: 59564
Title: Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification.
Author: Aleksi Rasanen, Markku Kuitunen, Erkki Tomppo, Anssi Lensu,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 169-182 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Habitat type mapping, Multispectral imagery, ALS, Object-based image analysis, Random forest classifier, Feature selection.
Abstract: We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach Segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below1m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity.
Location: TE 12 New Biology Building
Literature cited 1: Antonorakis, A.S. Richards, K.S. Brasington, J., 2008.Object-based land cover classification using airborne LiDAR .Remote Sens. Environ.112, 2988-2998. Arivazhagan, S., Ganesan, L., 2003. Texture Classification using wavelet transform. Pattern Recogn.Lett.24, 1513-1521.
Literature cited 2: Arroyo, L.A., Johansen, K., Armston, J., Phinn, S., 2010. Integration of LiDAR and Quickbird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical Savannas. For .Ecol. Manage.259, 598-606. Baatz, M., Schape, A., 2000. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: strobl, J., Blaschke, T., Griesebner, G. (Eds), Angewandte Geographische Informationsverarbeitung XII. Wichmann, Heidelberg, pp.12-23.

ID: 59563
Title: Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India.
Author: Mehul R. Pandya, Dhiraj B. Shah, Himanshu J. Trivedi, Nikunj P. Darji, R. Ramakrishanan, Sushma Panigrahy, Jai Singh Parihar, A.S. Kirankumar.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 160-168 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Kalpana-1, Land surface temperature (LST), MODTRAN, Single-channel, Thermal infrared.
Abstract: Indian geostationary satellite-Kalpana-1(K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5-12.5?m) observations of the Very High Resolution Radiometer (VHRR) sensor. The study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jimenez-Munoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmosheric Functions (AFs) that are dependent on transimisivity, upweilling and down welling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LSTderived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that sc algorithm was successful in capturing the prominent diurnal variations of 283-332 k in the LST at desert and agriculture experimental sites with a rmse of 1.6 k and 2.7 K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300 K, however a cold bias up to 7.9 K was observed in MODIS LST for higher LST value (310-330 K) over the hot desert region.
Location: TE 12 New Biology Building
Literature cited 1: Akhoondzadeh, M., Saradjian, M.R., 2008. Comparison of LST Mapping using MODIS and ASTER Images in Semi-Arid Area. The Int. Archives of Photogrammetry, vol. XXXVII, Part B8.Rem Sens& Spatial Information Sciences, Beijing, pp.873-876. Becker, F., 1987. The impact of spectral emissivity on the measurement of land surface temperature from a satellite .Int.J. Remote Sens.8, 1509-1522.
Literature cited 2: Becker, F., Li., Z.L., 1990. Toward a local split window method over land surface. Int.J. Remote Sens.11, 369-390. Becker, F., Li., Z.L., 1995. Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sens.Rev.12, 225-253.

ID: 59562
Title: A new metric for measuring structure-preserving capability of despeckling of SAR images.
Author: Xuezhi Yang, Kewei Wu, Yiming Tang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 143-159 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Synthetic aperture radar (SAR) images, Speckle noise, Performance assessment, Structure -preserving, Despeckling structure loss.
Abstract: In this paper, a new metric called despeckling structure loss (DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural features such as edges and textures. The ratio image of synthetic aperture radar (SAR) image to its despeckled result can clearly indicate the loss of structural features caused by the despeckling process. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of structural features in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points. The DSL is shown to a monotonic function of structure loss bounded between the best and worst cases, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. Experimental evaluations of the proposed metric have been carried out on simulated SAR images including one generated by SAR raw signal simulator. Despeckled results using five typical algorithms clearly demonstrate the efficiency of the DSL metric. In comparison, the commonly used despeckling metrics including the signal-to-mean-square error ratio, the edge correlation index, the Pratt ' s figure of merit, the structural similarity and the equivalent number of looks of ratio images, fail to keep a consistency with the structure loss shown in despeckled results as well as ratio images.
Location: TE 12 New Biology Building
Literature cited 1: Abramson, S.B., Schowengerdt, R.A., 1993.Evaluation of edge-prserving smoothing filters for digital image mapping. ISPRS J.Photogramm.Remote Sens.48 (2), 2-17. Achim, A., Kuruoglu, E.E., Zerubia, J., 2006. SAR image filtering based on the heavy tailed Rayleigh model. IEEE Trans.Image Process.15 (9), 2686-2693.
Literature cited 2: Achim, A., Tsakalides, P., Bezerianos, A., 2003. SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling. IEEE Trans. Geosci. Remote Sens.41 (8), 1733-1784. Aja-Fernandez, S., Alberola-Lopez, C., 2006. On the estimation of coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image process. 15 (9) 2964-2701.

ID: 59561
Title: Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images.
Author: Claire Marais Sicre, Frederic Baup, Remy Fieuzal
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 127-142 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Crop monitoring, Crop row orientation, Mathematical morphology, Directional filtering, Satellite image, Formosat-2, Panchromatic
Abstract: This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM ' 10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45? and 135?) and mathematical morphology algorithms. "Single -date" and "multi-temporal" approaches are considered. The single-data analyses confirm the good performances of the proposed method, the emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley, and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27>R?>0.99 and 7.15?> RMSE > 43.02?) for the summer crops (sunflower, corn and hemp). Results are strongly crop and date depends (0>R?>0.96, 10.22?>RMSE>80?), with a well-marked impact of flowering, irrigation equipment and /or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations(more than 45,000 ha) with an error inferior to 40?, associated to a confidence index ranging from 1 to 5 for each agricultural plot.
Location: TE 12 New Biology Building
Literature cited 1: Agam, N., Kustas, W.P., Evett, S.R., Colaizzi, P.D., Cosh, M.H., Mckee, L.G., 2012. Soil heat flux variability influenced by row direction in irrigated cotton. Adv.water Res. 50, 31-40. Amesz, B., Lausink, A., 1984. Satellite sensing aid upper Volta ' s drilling. World Water, 21-24.
Literature cited 2: Andrieu, B., Baret, F., Jacquemoud, S., Malthus, T., Steven, M., 1997. Evaluation of an improved version of SAIL model for simulating bidirectional reflectance of sugar beet canopies. Rem.Sens.Environ.60, 247-257. Baret, F., de Solan, B., Lopez-Lozano, R., Ma. K. Weiss, M., 2010. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5A? zenith angle: Theoretical considerations based on 3D architechture models and application to wheat crops. Agri. For. Meterol.150, 1393-1401.