ID: 59482
Title: Measuring deformation using SAR interferometry and GPS observables with geodetic accuracy : Application to Tokyo, Japan.
Author: Tamer Elgharbawi, Masayuki Tamura.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 156-165 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: InSAR, GPS, GSI, 2011 Tohoku earthquake, Land deformation measurement.
Abstract: This paper presents new methodology for correcting interferometric synthetic aperture radar (InSAR) deformation maps using GPS observables and products. The methodology presents a sequential procedure for correcting the errors presented in InSAR deformation maps such as troposphere delay, ionosphere delay and baseline error. The main target of this reserch is to measure land deformations with geodetic accuracy using only one L-band interferogram with the aid of GPS observables and products. The proposed methodology was tested on Tokyo bay area which has been affected by the 2011 Tohoku earthquake. The results were verified against deformations detected by GPS stations and geodetic triangulation network showing a standard deviation of 5.6 and 10.5 mm, respectively.
Location: TE 15 New Biology Building
Literature cited 1: Balmer, R., Hartl, P., 1998. synthetic Aperture Radar Interferometry. Inverse prob., R1-R54.
Brcic, R., Parizzi, A., Eineder, M., Bamler, R., Meyer, F., 2010. Estimation and compensation of Ionospheric Delay for SAR Interferometry. In: IGARSS IEEE, pp. 2908-2911.
Literature cited 2: Bricic, R., Parizzi, A., Eineder, M., Bamler, R., Meyer, F., 2011. Ionospheric effects in sar interferometry : an analysis and comparison of methods for their estimation. In: Geoscience and Remote Sensing Symposium (IGARSS). Vancouver, canada: IEEE International, pp. 1497-1500.
Buckley, S.M., Rosen, P., Hensely, S., Tapley, B., 2003. Land subsidence in Houston, Texas, measured by radar interferometry. J. Geophys. Res. 108 (8-1), 8-12.
ID: 59481
Title: Application of multispectral LiDAR to automated Virtual outcrop geology.
Author: Preston Hartzell, Craig Glennie, Kivanc Biber, Shuhab Khan.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 147-155 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Laser Scanning, LIDAR, Multispectral, Calibration, Radiometric, Classification.
Abstract: Terrestrial Laser Scanning (TLS) is valuable tool for creating virtual 3D models of geological outcrops to enable enhanced modeling and analysis of geologic strata. Application of TLS data is typically limited to the geometric point cloud that is used to create the 3D structure of the outcrop model. Digital Photography can then be draped on to the 3D model, allowing visual identification and manual spatial delineation of different rock layers. Automation of the rock type identification and delineation is desirable, and recent work has investigated the use of terrestrial hyperspectral photography for this purpose. However, passive photography, whether visible or hyperspectral, presents several complexities, including accurate spatial registration with the TLS point cloud data, reliance on sunlight for illumination, and radiometric calibration to properly extract spectral signatures of the different rock types. As an active remote sensing method, a radiometrically calibrated TLS system offers the potential to directly provide spectral information for each recorded 3D point,, independent of solar illumination. Therefore, the practical application of three radiometrically calibrated TLS systems with differing laser wavelengths, thereby achieving a multispectral dataset in conjunction with 3D point cloud data, is investigated using commercially available hardware and software. The radiometric calibration of the TLS intensity values is investigated and the classification performance of the multispectral TLS intensity and calibrated reflectance datasets evaluated and compared to classification performed with passive visible wavelength imagery. Results indicate that rock types can be successfully identified with radiometrically calibrated multispectral TLS data, with enhanced classification performance when fused with passive visible imagery.
Location: TE 15 New Biology Building
Literature cited 1: Alexander, V.V., Shi, Z., Islam, M.N., Ke, K., Freeman, M.J., Ifarraguerri, A., Meola, J., Absi, A., Leonard , J., Zadnik, J., Szalkowski, A,S., Boer, G.J., 2013. Power scalable >25W supercontinuum laser from 2to 2.5 ?m with near-diffraction -limited beam and low output variability. opt. Lett 38(13), 2292-2294.
Bachmann, C.M., Nicholas, C.R., Montes, M.J., Li, R.-R., Woodward , P., Fusina,R.A., Chen, W.., Mishra, V., Kim., W., Monty, J., Mcilhany, K., Kessler, K., Korwan, D., Miller, W.D, Bennert, E., smith , G., Gillis, D., Sellers, J., Parrish, C., Schwarzschild, A., Truitt, B., 2010. Retrieval of substrate bearing strength from hyperspectral imagery during the virginia coast reserve(VCR ' 07) multi-sensor campaign. Mar. Geodesy 33 (2-3), 101-116.
Literature cited 2: Bates, K.T., Manning, P.L., Vila, B., Hodgetts, D., 2008. Three-dimensional modelling and analysis of dinosaur trackways. Paleontology 51 (4), 999-1010.
Browell, E.V., Ismail, s., Grant, W.B., 1998. Differential absorption lidar(DIAL) measurements from air and space. Appl.Phys. B:Lasers opt. 67 (4), 399-410.
ID: 59480
Title: Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and Comparison.
Author: Wajahat Kazmi, Sergi Foix, Guillem Alenya, Hans Jorgen Andersen
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 128-146 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Leaf imaging, Depth, Exposure, Time-of-Flight, Stereo Vision, Sunlight.
Abstract: In this article weanalyze the response of Time-of-Flight (TOF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD camcube and swiss Ranger SR4000) is compared against selected stereo-correspondence algorathims(local correlation and graph cuts). PMD CamCube has better cancelation of sunlight , followed by CamBoard, while, Swiss Ranger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (Swiss Ranger).
Location: TE 15 New Biology Building
Literature cited 1: Alenya , G., Dellen, B., Torras, C., 2011. 3D modelling of leaves from color and ToF data for robotized plant measuring. In: IEEE International conferenceon Robotics and Automation , Shanghai, China, pp. 3408-3414.
Alenya, G., Dellen, B., Foix, S., Torras, C., 2013. Robotized plant probing: leaf segmentation utilizing time-of-flight data. Robot. Automat. Mag., IEEE 20 (3), 50-59.
Literature cited 2: Andersen , H.J., Reng, L., Kirk, K.,2005. Geometric plant proporties by relaxed stereo vision using simulated annealing. Comput. Electron. Agri.49(2), 219-232, ISSN: 01681699, doi:10.1016/j.compag.2005.02.015.
Astrand, B., Baerveldt, A., 2004, Plant recognition and localization using context information. In: proc. of the IEEE conference Mechatronics and Robotics, Luoyang, pp. 13-15.
ID: 59479
Title: Automated Parameterisation for multi-scale image segmentation on multiple layers.
Author: L. Dragut, O. Csillik, C. Eisank, D. Tiede
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 119-127(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Autmation, Imagery, object, Representation, GEOBIA, MRS
Abstract: We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, Where the scale factor in the segmentation, namely, the scale parameter(SP), increase with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis.
Location: TE 15 New Biology Building
Literature cited 1: Aguilar, M.A., Saldana, M.M., Aguilar, F.J., 2012. GeoEye-1 and WorldView -2 pansharpened imagery for object-based classification in urban environment.Int.J. Remote Sens. 34(7), 2583-2606.
Ardila, J.P., Bijker, W., Tolpekin, V. A, Stein, A., 2012. Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. Int.J. Appl.Earth obs. Geoinf. 15, 57-69.
Literature cited 2: Arvor, D., Durieux, L., Andres, S., Laporte, M.-A., 2013. Advance in geographic object -based image analysis with ontologies : a review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogramm. Remote Sen. 82, 125-137.
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.), Angew. Geogr. Info. Verarbeitung. Wichmann-verlag, Heidelberg, pp. 12-23.
ID: 59478
Title: Structured Sparse Method for Hyperspectral Unmixing.
Author: Feiyun Zhu, Ying Wang, Shiming Xiang, Bin Fan, Chunhong Pan
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 101-118(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyperspectral Unmixing (HU), Hyperspectral image analysis, structured sparse NMF(SS-NMF), Mixed pixel, Nonnegative Matrix Factorization(NMF)
Abstract: Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a structured sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighbouring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the art methods significantly.
Location: TE 15 New Biology Building
Literature cited 1: Belkin, M., Niyogi, P., 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems (NIPS) . MIT press, pp. 585-591.
Berry, M.W., Browne, M., L angville, A.N., Pauca, V.P., Plemmons, R.J., 2007. Algorithms and applications for approximate nonnegative matrix factorization. Comput. Statist. Data Anal. 30 (1), 155-173.
Literature cited 2: Bioucas-Dias, J.M., 2009. A variable splitting augmented lagrangian approach to linear spectral unmixing. In: Workshop on Hyperspectral Image and signal processing : Evolution in Remoting Sensing(WHISPERS). pp. 1-4.
Bioucas -dias et al., 2012. Hyperspectral unmixing overview: geometrical, Statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 5 (2), 354-379.
ID: 59477
Title: Remotely sensed biomass over steep slopes: An evaluation among successional stands of the Atlantic Forest, Brazil
Author: Jomar Magalhaes Barbosa, Ignacio Melendez-Pastor, Jose Navarro-Pedreno, Marisa Dantas Bitencourt.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 91-100(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Aboveground biomass, Forest succession, Tropical forest, Steep slope, Remote sensing.
Abstract: Remotely sensed images have been widely used to model biomass and carbon content on large spatial scales. Nevertheless, modelling biomass using remotely sensed data from steep slopes is still poorly understood. We investigated how topographical features affect biomas estimation using remotely sensed data and how such estimates can be used in the characterization of successional stands in the Atlantic Rainforest in the Southeastern Brazil. We estimated forest biomass using a modelling approach that included the use of both satellite data (LANDSAT) and topographic features derived from a digital elevation model (TOPODATA). Biomass estimates exhibited low error predictions (Adj. R?=0.67 and RMSE=35 Mg/ha) When combining satellite data with a secondary geomorphometric features variable, the illumination factor, which is based on hill shading patterns. This improved biomass prediction helped us to determine carbon stock in different forest successional stands. Our results provide an important source of modelling information about large -scale biomass in remaining forests over steep slopes.
Location: TE 15 New Biology Building
Literature cited 1: Adams, C., 2000. As rocas eo manejo da mata Atlantica pelos caicaras : uma revisao. Interciencia 25 (3), 143-150.
Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom. control 19 (6), 716-723.
Literature cited 2: Alves , L.F., Vieira, S.A., Scaranello, M.A., Camargo, P.B., Santos, F.A.M., Joly, C.A., Martinelli, L.A., 2010. FOorest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest(Brazil). For. Ecol. Manage. 260 (5), 679-691.
Anaya, J.A., Chuvieco, E., Palacios-orueta, A., 2009. Aboveground biomass assessment in colombia: a remote sensing approach. For. Ecol. Manage. 257(4) , 1237-1246.
ID: 59476
Title: Mapping the human footprint from satellite measurements in Japan
Author: Fan Yang, Bunkei Matsushita, Wei Yang, Takehiko Fukushima.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 80-90(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Human footprint, Impervious surface, Cropland, Temporal mixture analysis, MODIS, NDVI time series.
Abstract: Due to increasing global urbanization and climate change, the quantification of "human footprints" has become an urgent goal in the fields of biodiversity consetrvation and regional environment management. A human footprint is defined as the impact of a particular human activity on the Earth ' s surface, which can be represented mainly by impervious surfaces (related to industry and urbanization ) and cropland (related to agriculture). Here we present a method called sorted temporal mixture analysis with post classification (STMAP) for mapping impervious surfaces and cropland simultaneously at the subpixel level to fill the demand for precise human footprint information on a national scale. The STMAP method applies a four-endmember sorted temporal mixture analysis to provide the initial fractions of evergreeen forests, deciduous forests, cropland and impervious surfaces as first step. Endmembers are selected from the sorted temporal profiles of the MODIS- normalized difference vegetation index (NDVI), as guided by a principal component analysis. The yearly maximum land surface temperatures and averaged stable night time light are then statistically analyzed to provide the thresholds for post- classification to further separate cropland from deciduous forest and bare land from impervious surface. As the four outputs of STMAP, The fractions of forest, cropland, impervious surfaces, and bare land are derived. We used the reference maps of impervious surfaces and cropland obtained from the Landsat/TM and ALOS precise land-use/land-cover map at the subpixel level to evaluate the performance of the proposed method respectively. Historical satellite images with high spatial resolution wewre used to further evaluate the cropland results derived with the STMAP metthod. The results showed that the STMAP method has promising accuracy for estimating impervious surfaces and 9.8% for the estimation of cropland. Our findings can extend the applications of remote sensing technologies in ecological research and environment management on a large scale.
Location: TE 15 New Biology Building
Literature cited 1: ALOS LULC map, 2013. ALOS precise land -use /land-cover map(ALOS LULC map) of Japan, produced by the Earth observation Research Center ( EORC), Japan Aerospace Exploration Agency (JAXA), <http://www.eroc.jaxa.jp/ALOS/IUIC/IUIC-jindex.htm>(accessed 04.01.13)
Arnold, C.L., Gibbons, C.J., 1996. Impervious surface coverage: the emergence of a key environmental indicator.J.AM.Plan. Assoc. 62(2), 243-258.
Literature cited 2: Bauer, M.E., Loffelholz, B.C., Wilson, B., 2007. Estimating and mapping impervious surface area by regression analysis of Landsat imagery.In: Weng, Q. (Ed.), Remote sensing of Impervious surfaces. CRC press , Boca Raton, Florida, pp.3-19.
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, 239-258.
ID: 59475
Title: None
Author: Chaoyang WU, Alemu Gonsamo, Fangmin Zhang, Jing M. Chen
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 69-79 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Enhanced vegetation index, Flux,, Gross Primary Production, Remote Sensing, Climate change, Carbon cycle.
Abstract: Remote sensing of veggetation gross primary production (GPP) is an important step to analyze terrestrial carbon (c) cycles in response to changing climate. The availability of global networks of c flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-fores (NF ), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greeness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Image Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using stastical parameters of both EVI and LST fitted for different PFTs. O ur results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF AND EF sites, respectively . The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates tthe potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosyastems globally.
Location: TE 15 New Biology Building
Literature cited 1: Barr, A.G., Black., T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., 2004. Interannual variability in the leaf area index of boreal aspen -hazelnut forest inrelation to net ecosystem production. Agric. For. Meteorol. 126(3-4), 237-255
Beer, c., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M.A, Baldocchi, D., Bonan., G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., lomas, M., Luyassaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E.,, Viovy, N., Williams, C., Ian Woodward, F., Papale, D., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329 (5993), 834-838.
Literature cited 2: Beets, A.K., Zhao, M., Dirmeyer, P.A., Beljaars, A.C.M., 2006. Comparison of ERA40 and NCEP/DOE near -surface data sets with other ISLSCP-11 data sets. J. Geophys. Res. 111, D 22S04. http://dx.doi.org/10.1029/2006JD007174.
Chen, J.M., Mo, G., P isek, J., Liu, J., Deng, F., Ishizawa, M., Chan, D., 2012. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global Biogeochem. Cycles 26, GB1019. http:// dx.doi.org/10.1029/2010GB003996.
ID: 59474
Title: Identification of multi- scale corresponding object -set pairts between two polygon datasets with hierarchical co-clustering.
Author: Young Huh, Jiyoung Kim, Jeabin Lee, Kiyun Yu, Wenzhong Shi
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 60-68 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Multi-scale object-set matching , Laplacian-graph embedding, Hierarchical co-clustering, Composite NDVI image, Forest inventory map, Geographic object-based image analysis.
Abstract: In this paper , We propose a means of finding multi-scale corresponding object-set pairs between two polygon datasets by means of hierarchical co-clustering. This metthod converts the intersection -ratio -based similarities of two objects from two datasets , one from each datasets, one from each dataset, into the objects ' proximity in a geometric space using a lapalcian -graph embedding technique. In this space , the method finds hierarchical object clusters by means of agglomerative hierarchical clustering and separetes each cluster into object-set pairs according to the datasets to which the object-set pairs. We applied the proposed method to the segmentation result of a composite image with 6 NDVI images and a forest inventory map. Regardless of the different origins of the datasets, the proposed method can find geometrically corresponding object -set pairs which represent hierarchical distinctive forest areas.
Location: TE 15 New Biology Building
Literature cited 1: Arbiol, R., Zhang , Y., V.,2006. Advanced classification techniques: a review . In: proc. ISPRS commission v11 Mid-term symposium , Enschede, NL, 8-11 May.( on CDROM).
Bel Hadj Ali, A., 2000. Measures entre objects surfaciques: application a laqualification desliens d ' appariement. Bull. Inf.de I ' I GN 71, 33-54.
Literature cited 2: Belkin, M., Niyogi, P., 2003. Laplacian eigenmaps for dimensionality reduction and data represention . Neural Comput. 15 (6), 1111373-1396.
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogrammetry Remote Sensing 65 (1), 2-16.
ID: 59473
Title: Detecting Sirex noctilio grey-attacked and lighting -struck pine trees using airborne hyperspetral data , random forest and support vector machines classifiers
Author: Elfaith M. Abdel -Rahman, Onisimo Mutanga, Elhadi Adam, Riyad Ismail
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 48-59 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Sirex grey stage, Lighting damage, pinus spp., Hyperspectral data, Random forest, Support vector machines.
Abstract: The visual progression of sirex (sirex noctilio) infestation symptoms has been categorized into three distinct infestation phases , namely the green, red , and grey stages. the grey is the final stage which leads to almost complete defoliation resulting in dead standing trees or snags. dead standing pine trees however ,could also be due to the lighting damage. Hence, the objective of the present study was to distinguish amongst healthy, sirex grey-attacked and lighting-damaged pine trees using Asia Eagle hyperspectral data, random forest (RF) and support vector machines (SVM) classifiers. our study also presents an opportunity to look at the possibility of separting amongst the previously menttioned pine trees damage classes and other landscape classes on the study area. The results of the present study revealed the robustness of the two machine learning classifiers with an overall accuracy of 74.50% (total disagreement =26%) for RF and 73.50% (total disagreement=27%) for SVM using all the remaining ASIA EAGLE spectral bands after removing the noisy ones. When the most useful spectral bands as measured by RF were exploited, the overall accuracy was considerably improved ; 78% (total disagreement =22%) for RF and 76.50% (total disagreement =24%) for SVM. There was no significant difference between the performances of the two classifiers as demonstrated by the results of McNemar ' s test (chi-sqared ; x?=0.14, and 0.03 when all the remaining ASIA EAGLE wavebands, , after removing the noisy ones and the most iimportant wavebands were used, respectively) . This study concludes that ASIA Eagle data classsified using RF and SVM algorithms provide relatively accurate information that important to the forest industry for making informed decision regarding pine plantations health protocols.
Location: TE 15 New Biology Building
Literature cited 1: Adam , E.M ., Mutunga, O., R ugege, Ismail, R., 2012. Discriminating the papyrus vegetation (cyperous papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP. int. J. Remote sens. 33, 552-569.
Agarwal , G., Sarup., J., 2011. Comparision of QUAC and FLASSSH atmospheric correction modules on EO-1 hypersion data of sanchi. Int.J. Adv. Eng. sci. Technol. 4, 178-186.
Literature cited 2: Agresti, A., 1996. An introducion to categorical Data Analysis. John Wiley, New York.
Bandos, T.V., Bruzzone , L., Camps-valls, G., 2009, Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans. Geosci. Remote Sens. 47, 862-873
ID: 59472
Title: Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China
Author: Liguo Zhou, Dar A. Roberts, Weichun Ma, Hao Zhang, Lin Tang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 41-47 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Hyperspectral , HJ-1A, satellite, Three -band model, chla, Dianshan lake.
Abstract: Based on in situ water sampling and field spectral measurements in Dianshan lake, a semi-analytical three-band algorithm was used to estimate chlorophylla (chla) content in case 11 waters. The three bands selected to estimate chla for high concentrations included 653, 691 and 748 nm. An equation, based on the diferrence in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [Rrs?? (653) - Rrs??(691)] Rrs(748), explained 85.57% of variance in chla concentration with a root mean square error (RMSE) of <6.56 mg/m?. In Order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (hsi) data were analyzed using comparable wavelengths selected from the in situ data[B67??(656) - b80??(716)] B87(753). This model accounted for 84.3% of chla variation, estimating chla variation, estimating chla concentrations with an RMSE of <4.23 mg/m?. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher chla concentration waters.
Location: TE 15 New Biology Building
Literature cited 1: Brando, V.E., Dekker, A.G., 2003. Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality .IEEE Trans . Geosci. Remote sen. 41 (6), 1378-1387.
Chen, S., Fang, L., Li, H., W., Huang, W., 2011. Evaluation of a three -band model for estimating chlorophyll -a concentration in tidal reaches of the pearl river estuary, china. ISPRS .J. Photogr. Remote sens.66(3), 356-364
Literature cited 2: Awrangjeb, M., Zhang , C., Fraser, C.S., 2012. Building detection in complex scenes thorough effective seperation of buildings from trees. Photogramm. Eng. Rem. Sens. 78 (7), 729-745.
Cheng, X., Li., X., 2010. Long -term changes in nutrients and phytoplanktonresponse in lake dianshan, a shallow temperature lake in china. J. Fresh water Ecol.25(4), 549-554.
ID: 59471
Title: Automatic registration of optical imagery with 3D Li DAR data using statistical similarity
Author: Ebadat G., Parmehr, Clive S. Fraser, Chunsun Zhang , Joseph Leach
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 28-40(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Registration, Li DAR, Point cloud, optical imagery, Mutual Information
Abstract: The development of robust and accurate methods for automatic registration of optical imagery and 3D li DAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of mutual information (MI), which exploits the statistical dependency between same-and multi -modal datasets to achieve accurate registration . The MI based similarity measures quantify dependencies between aerial imagery , and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3Dpoint clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registraation between optical imagery and LiDAR data. The effectiveness of local versus global similarity measure is also investigated, as are the transformation models is involved in the registrion process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover , and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI -based approach to automated registration of multi-sensor, multi-temporal and multi resolution remote sensing data for a wide range of applications.
Location: TE 15 New Biology Building
Literature cited 1: Abdel-aziz, Y.i., Karara, H.M., 1971. Direct Linear transformation from comparator coordinates into object space coordinates in close -range photogrammetry. In: proc. ASP Symposium on Close-range photogrammetry, Falls church , V.A
Al-Manasir, K., Fraser, C.S., 2006. Automatic registration of terrestrial Laser Scanner data via imgery. Photogramm. Rec 21(115), 255-268
Literature cited 2: Awrangjeb, M., Zhang , C., Fraser, C.S., 2012. Building detection in complex scenes thorough effective seperation of buildings from trees. Photogramm. Eng. Rem. Sens. 78 (7), 729-745.
Baltsavias , E.P., 1999a. Comparison between photogrammetry and Laser Scanning. ISPRS J. Photogramm. Rem. Sens. 54 (2), 83-94.
ID: 59470
Title: A GIHS - based spectral preservation fusion method for remote sensing images using edge restored spectral modulation
Author: Xiran Zhou, Jun Liu, Shuguang Liu, Lei Cao, Quiming Zhou, Huawen Huang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 16-27(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Image fusion, Generalized intensity -hue- saturation, Edge restored spectral modulation, Spectral modulation , spectral distortion, Image quality evaluation, Remote Sensing
Abstract: High Spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity-hue-saturation (IHS) transform and the generalized IHS ( GIHS) , which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS -based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Guassian function to extract spatial details and conduct SM of multispectral (MS) images. This metthod yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.
Location: TE 15 New Biology Building
Literature cited 1: Aizzi, B., Baronti, S., Selva, M., 2007. Improving component substitution pansharpening through multivariate regression of MS+pan data. IEEE Trans. Geosci. Remote Sens. 45 (10) , 3230-3239.
Alparone , L., A iazzi, B., Baronti, S., Garzelli, A., Nencini., F., Selva, M., 2008. Multispectral and Panchromatic data fusion assesment without reference. P hotogramm . Eng. Remote Sens. 74 (2) , 193-200.
Literature cited 2: Chen, S.H., Zhang, R.H., Su, H.B., 2009. Scaling-up transformation of multisensor images with multiple resolutions. sensors 9, 1370-1381.
Chibani, Y., Houacine , A., 2002. The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images. Int. J. Rem. Sens. 23 (18), 3821-3833
ID: 59469
Title: Multiple-entity based classification of airborne laser scanning data in urban areas.
Author: S. xu, G. Vosselman, S.oude Eilberink
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 88, 1-15(2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Airborne laser scanning, Classification, Multiple-entity, Roof, Wall, Vegetation, Ground, Water, Building.
Abstract: There are two main challenges when it comes to classifying airborne laser scanning(ALS) data. the first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calcutate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple -entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. we also compared the performance of the multiple-entity based method to the single -entity based method.
Features have been extracted , in most previous work, from a single entity in ALS data; either from a point or from grouped points. in our method, we extract features fgrom three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four -step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. Duiring the contextual reasoning , the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.
Five -supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple -entity stategy achieves slightly higher overall accuracy and achieves much higher accuracy of vegetation improves by 3.3% with the rule -based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy and higher accuracy in vegetation in comparison to using only the point -wise classification method for all five classifiers.
Meanwhile, we compared the performance of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and minimum accuracy of 90.0% for the water, vegetation, wall and defined object classes. Notably, the accuracy of the roof element class in only 70% with the rule-based method,or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous asssignments are not fatal errors because both a roof element and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule -based classifier is 85.5%, which is 6% lower than that with pulse count information.
Location: TE 15 New Biology Building
Literature cited 1: Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Heitz, G., Ng, A., 2005. Discriminative learning of markov random fields for segmentation of 3 D scan Data. In: IEEE conference on Computer Vision and Pattern Recognition , San Diego, CA ,pp. 169-176
Breiman, Leo., 2001. Random forests. Mach. Learn. 45 (1), 5-32.
Literature cited 2: Brodu, N., Lague, D., 2012. 3D terrestrial lidar data classification of complex natural scenes using a multiple-scale dimensionality criterior: applications in geomorphology.ISPRS J. Photogramm. Remote sens.68,121-134.
Chehata, N., guo, L., Mallet, M., 2009. Airborne lidar feature selection for urban classification using random forests. Int. Arch. photogramm. Remote Sens. Spatial Inf. Sci.38 (part 3/w8), 207-212.
ID: 59468
Title: An innovative support vector machine based method for contextual
Author: Rogerio Galante Negri , Lucioano Vieira Dutra , Sidnei joao siqueira sant ' Anna
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 216-228 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Image classification, contextual information, support vector machine
Abstract: Several remote sensing studies have adopted the support vector machine (SVM) metod for image classification. Althogh the original formulation of the SVM method does not incorporate contextual informatiion, there are different proposals to incorporate this type of information, there are different proposals to incorporate this type of information into it. Usually, these proposals modify the SVM training phase or make an integeration of SVM classifications using stochastic models. This study presents a new perspective on the development of contextual SVMs. The main concept of this proposed method is to use the contextual information to displace the separation hyperplane, initially defined by the traditional SVM. This displaced hyperplane could cause a change of the class initially assigned to the pixel. To evaluate the classification effectiveness of the proposed method a case study is presented comparing the results with the standard SVM and the SVM post- processed by the mode (majority) filter. An ALOS/ PALSAR image, PLR mode, acquired over an Amazon area was used in the experiment. considering the inner area of test sites, the accuracy results obtained by the proposed method is better than SVM post- processed by the mode filter. The proposed method, however, produces better results than mode post- processed SVM when considering the classification near the edges between regions. one draw of the method is the computational cost of the proposed method is significantly greater than the compared methods.
Location: TE 15 New Biology Building
Literature cited 1: Besag, J., 1986. On the stastical analysis of dirty pictures. J. Roy. Stat. Soc., 259-302.
Besbes, o., Boujemaa, N., Belhadj, Z., 2009. contextual classification of high resolution satellite images. In: proc. IEEE symposium on computional intelligence for image processing , Nashville, 30-2 April ,pp.41-47.
Literature cited 2: Bovolo, F. , Bruzzone , L., 2005. A context- sensitive technique based on support vector machine for image classification. Lect. Notes comput. Sci. 3376, 260-265.
Bovolo, F., Bruzzone,L., Marconicini , M., 2006. A novel context -sensitive SVM for classification of remote sensing images . In: proc .IEEEInternational Geoscience and Remote Sensing Symposium, Denver , 30-6 August ,pp. 2498-2501.