Enter records

ID: 59497
Title: Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut
Author: Sven Oesau, Florent Lafarge, Pierre Alliez.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.68-82 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Indoor scenes, 3D reconstruction, Laser scanning, Multi-scale line extraction, Graph cut, Energy minimization.
Abstract: We Present a method for automatic reconstruction of permanent structures, such as walls, floors and ceilings, given a raw point cloud of an indoor scene. The main idea behind our approach is a graph-cut formulation to solve an inside/outside labeling of a space partitioning. We first partition the space in order to align the reconstructed models with permanent structures. The horizontal structures are located through analysis of the vertical point distribution, while vertical wall structures are detected through feature preserving multi-scale line fitting, followed by clustering in a Hough transform space. The final surface is extracted through a graph-cut formulation that trades faithfulness to measurement data for geometric complexity. A series of experiments show watertight surface meshes reconstructed from point clouds measured on multi-level buildings.
Location: TE 12 New Biology Building
Literature cited 1: Adan, A., Huber, D., 2011. 3D reconstruction of interior wall surfaces under occlusion and clutter. In: International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, IEEE computer society, Los Alamitos, CA, USA. Pp.275-281. Agarwal, P.K., Sharir, M., 1998. Arrangements and their applications. In: Handbook of Computational Geometry. Elsevier science publishers B.V. North -Holland, pp. 49-119
Literature cited 2: Belnap, J., Welter, J.R. Grimm, N.B., Barger, N., Ludwig, J.A., 2005. Linkages between microbial and hydrological processes in arid and semiarid watersheds. Ecology 86,298-307. Belnap, J., Lange, O.L., 2003. Biological Soil Crusts: Structure, Function, and Management. Springer, Berlin Heidelberg New York. Boulc ' h, A., Marlet, R., 2012. Fast and robust normal estimation for point clouds with sharp features. Comput. Graph. Forum 31, 1765-1774. Boykov, Y., Veksler, o., Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239

ID: 59496
Title: Advanced image processing methods as a tool to map and quantify different types of biological soil crust.
Author: Emilio Rodr?guez-Caballero, Paula Escribano, Yolanda Canton.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.59-67(2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Biological soil crust mapping, Surface cover quantification, Hyperspectral imagery, Dryland , Multiple endmember spectral mixture analysis (MESMA).
Abstract: Biological soil crusts (BSCs) modify numerous soil surface properties and affect many key ecosystem processes. As BSCs are considered one of the most important components of semiarid ecosystems, accurate characterisation of their spatial distribution is increasingly in demand. This paper describes a novel methodology for identifying the areas dominated by different types of BSCs and quantifying their relative cover at subpixel scale in a semiarid ecosystem of SE Spain. The approach consists of two consecutive steps: (I) First, Support Vector Machine (SVM) classification to identify the main ground units , dominated by homogenous surface cover (bare soil, Cynobacteria BSC, lichen BSC, green and dry vegetation), which are of strong ecological relevance. (ii) Spectral mixture analysis (SMA) of the ground units to quantify the proportion of each type of surface cover within each pixel, to correctly characterize the complex spatial heterogeneity inherent to semiarid ecosystems. SVM classification showed very good results with a Kappa coefficient of 0.93%, discriminating among areas dominated by bare soil, cynobacteria BSC, lichen BSC, green and dry vegetation. Subpixel relative abundance images achieved relatively high accuracy for both types of BSCs (about 80%), whereas general overestimation of vegetation was observed. Our results open the possibility of introducing the effect of presence and of relative cover of BSCs in spatially distributed hydrological and ecological models, assessment and monitoring aimed at reducing degradation in these areas.
Location: TE 12 New Biology Building
Literature cited 1: Asner, G.P., 2004. Biophysical Remote Sensing Signatures of Arid and Semiarid Ecosystms. In: Ustin, S.L., (Ed.), Remote Sensing for Natural Resource Management and Environment Monitoring. John Wiley and sons Inc. Belnap, J., 2006. The Potential roles of biological soil crusts in dryland hydrological cycles. Hydrol. Process. 20, 3159-3178.
Literature cited 2: Belnap, J., Welter, J.R. Grimm, N.B., Barger, N., Ludwig, J.A., 2005. Linkages between microbial and hydrological processes in arid and semiarid watersheds. Ecology 86,298-307. Belnap, J., Lange, O.L., 2003. Biological Soil Crusts: Structure, Function, and Management. Springer, Berlin Heidelberg New York.

ID: 59495
Title: Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland
Author: Beth Cole, Julia McMorrow, Martin Evans
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.49-58(2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Vegetation, Ecology, Hyper spectral, High resolution, Spectral, Monitoring
Abstract: Recognising the importance of the timing of image acquisition on the spectral response in remote sensing of vegetated ecosystems is essential. This study used full wavelength, 350-2500 nm, field spectroscopy to establish a spectral library of phenological change for key moorland species, and to investigate suitable temporal windows for monitoring upland peatland systems. Spectral responses over two consecutive growing Seasons were recorded at single species plots for key moorland species and species sown to restore eroding peat. This was related to phenological change using narrowband vegetation indices (Red Edge Position, Photochemical Reflectance Index, Plant Senescence Reflection Index and Cellulose Absorption Index) ; that capture green-up and senescence related changes in absorption features in the visible to near infrared and the shortwave infrared. The selection of indices was confirmed by identifying the regions of maximum variation in the captured reflectance across the full spectrum. The indices show change in the degree of variation between species occurring from April to September, measured for plant functional types. A discriminant function analysis between indices and plant functional types determines how well each index was able to differentiate between the plant functional groups for each month. It identifies April and July as two months where the species are most separable. What is presented here is not one single recommendation for the optimal temporal window for operational monitoring , but a fuller understanding of how the spectral response changes with the phenological cycle, including recommendations for what indices are important throughout the year.
Location: TE 12 New Biology Building
Literature cited 1: Asner, G.P., 1998. Biophysicical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64 (3), 234-253. Baranoski, G.V.G., Rokne, J.G., 2005. A practical approach for estimating the red edge position of plant leaf reflectance. Int. J. Remote Sens. 26 (3), 503-521.
Literature cited 2: Bellamy, P.H., Loveland, P.J., Bradley, R.I., Lark, R.M., and Kirk. G.J.D., 2005. Carbon losses from all soils across England and Wales 1978-2003. Nature 437(7056), 245-248. Blackburn, G.A., 2006. Hyperspecral remote sensing of plant pigments. J. Exp. Bot. 58(4), 855-867.

ID: 59494
Title: A multi- index learning approach for classification of high-resolution remotely sensed images over urban areas.
Author: Xin Huang, Qikai Lu, Liangpei Zhang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.36-48 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: High spatial resolution, Classification, SVM, Morphological, Texture, Feature extraction
Abstract: In recent years, it has been widely agreed that spatial features derived from textural , structural , and object-based methods are important information sources to complement spectral properties for accurate urban classification of high- resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high -dimensional feature space. The high -dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low- dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically , two categories of indices are proposed in the study: (1) Primitive indices (pI) : High-resolution urban scenes are represented using a group of primitives(e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (vI) : A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low -dimensional and semantic indices are then learned via the multi -kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, Quick Bird, WorldView -2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low - dimensional feature space, and, provide a practical strategy for processing large-scale high -resolution images.
Location: TE 12 New Biology Building
Literature cited 1: Aguera, F., Aguilar, A.M., 2008. Using texture analysis to improve perpixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogramm Remote Sens. 63 (6), 635-646. Awarangjeb, M., Ravanbakhsh, M., Fraser, S.C., 2010. Automatic detection of residential buildings using LIDAR data and multispectral imagery. ISPRS J. Photogram. Remote Sens. 65(5), 457-46
Literature cited 2: Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogram. Remote Sens. 65(1), 2-16. Bruzzone, L., Carlin, L., 2006. A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans. Geosci. Remote Sens. 44

ID: 59493
Title: 3D change detection at street level using mobile laser scanning point clouds and terrestrial images.
Author: Rongjun Qin, Armin Gruen
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.23-35 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: 3D change detection , Terrestrial images, Image -based mobile mapping system, Topographical LiDAR , Graph cuts, Superpixel
Abstract: Automatic change detection and geo -database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images , but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-dat- abases include 3D street -level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection , but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference , and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost- efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi -automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and reprojected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes , robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.
Location: TE 12 New Biology Building
Literature cited 1: Achanta, R., Shaji, A.,Smith, K., Lucchi, A., Fua, P., Susstrunk, S., 2012. SLIC Superpixels compared to state -of- the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274-2282. Arastounia, M., 2012.Automatic Classification of LiDAR PointClouds in A Railway Environment. Universsity of Twente, Neterlands, pp.83.
Literature cited 2: Bouziani, M., Goita, K., He, D.-c., 2010. Automatic Change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS J. Photogrammetry Remote Sensing 65 (1), 143-153. Boykov, Y., Kolmogorov, V., 2004. An experimental comparison of min-cut /max flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124-1137

ID: 59492
Title: Classfication of dual - and single polarized SAR images by incorporating visual features.
Author: Stefan Uhlmann, Serkan Kiranyaz
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.10-22 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Synthetic aperture radar, Classification, Image analysis, Visual features, Color,
Abstract: Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification . In this paper, we investigate the application of several individual visual features for a novel supervised classification application of dual-and single -polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual -and single-pol image data.
Location: TE 12 New Biology Building
Literature cited 1: Attema, E., Davidson, M., Snoeji, P., Rommen, B., Floury, N., Agency, E.S., Box, P.O., 2009. SENTINEL -1 Mission Overview. In: proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , Cape Town , South , Africa , 12-17 July , pp 36-39. Aytekin , O., Koc, M., Ulusoy, I., 2013. Local Primitive pattern for the classification of SAR images. IEEE Trans. Geosci. Remote Sens 51 (4), 2431-244.
Literature cited 2: Chang, c. -c. Lin, c. -J., 2011. Libsvm. ACM Trans. Intell . Syst. Technol. 2 (3), 1-27. Cloude, S.R., Pottier, E., 1996. A review of target decomposition theorems in radar Polarimetry. IEEE Trans. Geosci. Remote Sens. 34 (2), 498-518

ID: 59491
Title: Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery.
Author: S. Freire, T. Santos, A. Navarro, F. Soares, J.D. Silva, N. Afonso, A. Fonseca,J. Tenedorio
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 90.1-9 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: QuickBird ,Feature extraction ,Buildings,Urban, Accuracy, Lisbon
Abstract: Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination , counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore , the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone.The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened Quick-Bird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1: 1000, 1: 5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1: 10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1: 10,000, while no buildings were complaint with the specification for scale 1: 1000.
Location: TE 12 New Biology Building
Literature cited 1: Awrangjeb, M., Ravanbakhsh, M., Fraser , C., 2010. Automatic detection of residential buildings using LIDAR data and multispectral imagery. ISPRS J. Photogramm. Remote Sens. 65(5), 457-467 Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004.Multiresolution, object -oriented fuzzy analysis of remote sensing data for GIS -ready information. ISPRS J. Photogramm. Remote sens. 58(3-4), 239- 258
Literature cited 2: Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J., Photogramm. Remote sens. 65(1), 2-16. Congalton, R.G., Green, K., 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second ed. CRC/Lewis press, Boca Raton FL, USA.

ID: 59490
Title: Effects of green space spatial pattern on land surface temperature : Implications for sustainable urban planning and climate change adaptation
Author: Matthew Maimaitiyiming, Abduwasit Ghulam , Tashpolat Tiyip, Filiberto Pla, Pedro Lattore-Carmona,Umut Halik,Mamat Sawut,Mario Caetano
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.59-66 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Land surface temperature , Landscape metrics,Normalized mutual information measure, Remotesensing ,Sustainable urban planning,Urban heat island ,Urban greenspace
Abstract: The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate.In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery , and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650, and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.
Location: TE 12 New Biology Building
Literature cited 1: Aishan, T., Halik, U., Cyffka, B., Kuba, M., Abliz, A., Baidourela, A., 2013. Monitoring the hydrological and ecological response to water diversion in the lower reaches of the Tarim River. Northwest china. Quatern. Int. 311, 155-162. Alberti, M., 2005. The effects of urban patterns on ecosystem function. Int. Regional Sci. Rev. 28, 168-192.
Literature cited 2: Allaby, M., 2008. A Dictionary of Earth Sceinces, third ed. Oxford University press Inc., Newyork, pp 460. Arnfield , A.J., 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. climatol. 23, 1-26.

ID: 59489
Title: Above ground biomass estimation inan African tropical forest with lidar and hyperspectral data.
Author: Gaia Vaglio Laurin, Qi Chen , Jeremy A. Lindsell, David A. Coomes, Fabio Del Frate,Leila Guerriero,Francesco Pirotti,Riccardo Valentini
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.49-58 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Lidar, Hyperspectral , Forestry, Africa, Biomass
Abstract: The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R? =0.70) improved the model based on lidar alone (R?= 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands had limited predictive power (R?= 0.36) when used alone. This analysis proves the efficiency of using PLSR with small -footprint lidar and high quality ground truth data is crucial for lidar - based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area.
Location: TE 12 New Biology Building
Literature cited 1: Adam, E., Mutanga, O., 2009. Spectral discrimination of Papyrus vegetation (cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS J. Photogramm. Remote sens. 64(6), 612-620. Anderson, J.E.., Plourde, L.C., Martin , M.E., Braswell , B.H., Smith, M.L., Dubayah, R.O.,Hofton, M.A., Blair, J.B., 2008. Integrating waveform LiDAR with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112, 1856-1870.
Literature cited 2: Asner, G.P., Martin, R.E., 2008. Spectral and chemical analysis of tropical forests: scaling from leaf canopy levels. Remote Sens. Environ. 112, 3958-3970. Asner , G., Flint, Hughes .R., Varga , T., Knapp, D., Kennedy-Bowdoin , T., 2009. Environmental and biotic controls over aboveground biomass throughout a tropical rain forest . Ecosystems 12 (2), 261-278.

ID: 59488
Title: Efficient, Simultaneous detection of multi -class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding
Author: Junwei Han, Peicheng Zhou, Dingwen Zhang, Gong Cheng, Lei Guo, Zhenbao Liu, Shuhui Bu, Jun Wu.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.37-48 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Geospatial target detection, Visual Saliency, Discriminative sparse coding.
Abstract: Automatic detection of geospatial targets in clustered scenes is a profound challenge in the field of aerial and satellite image analysis. In this paper, we propose a novel practical framework enabling efficient and simultaneous detection of multi-class geospatial targets in remote sensing images( RSI) by the integration of visual saliency modeling and discriminative learning of sparse coding. At first, computational saliency prediction model is built via learning a direct mapping from Variety of visual features to a ground truth set of salient objects in geospatial images manually annotated by experts . The output of this model can predict a small set of target candidate areas. Afterwards , in contrast with typical models that are trained independently for each class targets, we train a multi-class object detector that can simultaneously localize multiple targets from multiple classes by using discriminative sparse coding. The Fisher discrimination criterion is incorporated into the learning of a dictionary, which leads to a set of discriminative sparse coding coefficients having small within-class scatter and big between -class scatter. Multi -class classification can be therefore achieved by the reconstruction error and discriminative coding coefficients. Finally, the trained multi-scale object detector is applied to those target candidate areas instead of the entire image in order to classify them into various categories of target, which can significantly reduce the cost of traditional exhaustive search. Comprehension evaluations on satellite RSI data base and comparisons with a number of state -of -the -art approaches demonstrate the effectiveness and efficiency of the proposed work.
Location: TE 12 New Biology Building
Literature cited 1: Achanta, R., Estrada , F., Wils , P., Susstrunk , S., 2008. Salient region detection and segmentation. In: proceedings of the 6 th International Conference on Computer Vision Systems (ICVS 2008), Santorini, Greece, 12-15 May, pp. 66-75. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S., 2009. Frequncy-tuned salient region detection. In: Proceedings of the 2009 computer vision and pattern recognition, 2009 (CVPR 2009), Miami, Florida, USA, 20-25 June, pp. 1597-1604.
Literature cited 2: Akcay, H.G., Aksoy, S., 2008. Automation detection of geospatial objects using multiple hierarchical segmentations. IEEE Trans. Geosci. Remote Sens. 46 (7), 2097-2111. Bhagavathy, S., Manjunath, B.S., 2006. Modeling and detection of geospatial objects using texture motifs. IEEE Trans. Geosci. Remote sens. 44(12), 3706-3715.

ID: 59487
Title: UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification.
Author: : Weiwei sun, Avner Halevy, John J. Benedetto, Wojceich Czaja, Chun liu, Hangbin Wu, Beiqi Shi, Weiyue Li.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.25-36 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Non-linear dimensionality reduction, UL-Isomap, Lisomap, Hyperspectral imagery classification, Vector quantization, Landmark Selection
Abstract: The paper proposes an upgraded landmark-isometric mapping (UL-Isomap) method to solve the two problems of landmark selection and computational complexity in dimensionality reduction using land mark isometric mapping ( LIsomap) for hyperspectral imagery (HSI) classification. First, the vector quantization method is introduced to select proper landmarks for HSI data. The approach considers the variations in local density of pixels in the spectral space. It locates the unique landmarks representing the geometric structures of HSI data. Then, random projections are used to reduce the bands of HSI data. After that, the new method incorporates the Recursive Lanczos Bisection (RLB) algorithm to construct the fast approximate k - nearest neighbor graph.The RLB algorithm accompanied with random projections improves the speed ofneighbour searching in UL- Isomap . After constructing the geodesic distance graph between landmarks and all pixels, the method uses a fast randomized low-rank approximate method to speed up the eigenvalue decomposition of the inner-product matrix in multidimensional scaling. Manifold coordinates of landmarks are then computed. Manifold coordinates of non-landmarks are computed through the pseudo inverse transformation of landmark coordinates. Five experiments on two different HSI datasets are run to test the new UL-Isomap method. Experimental results show that UL-Isomap surpasses LIsomap , both in the overall classification accuracy (OCA) and in computational speed, with a speed over 5 times faster. Moreover, the UL-Isomap method , when compared against the isometric mapping (Isomap) method, obtains only slightly lower OCAs.
Location: TE 12 New Biology Building
Literature cited 1: Adam, E., Mutanga, O., Rugege, D., Ismail, R., 2012. Discriminating the papyrus vegetation (Cyperus Papurus L.) And its co-existence species using random forest and hyperspectral data resampled to HYMAP. Int. J. Remote Sens. 33, 552-569. Bachmann, c.m., Ainsworth, T.L., Fusina, R.A., 2005. Eeploiting manifold geometry in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 43, 441-454.
Literature cited 2: Bachmann, C.M., Ainsworth, T.L., Fusina , R.A., 2006. Improved manifold coordinate representations of large-scale hyperspectral scenes. IEEE Trans. Geosci. Remote Sens. 44, 2786-2803. Bachmann , C.M., Ainsworth , T.L., Fusina , R.A., Montes, M.J., Bowels, J.H., Korwan, D.R., Gillis, D.B., 2009. Bathymetric retrieval from hyperspectral scenes. IEEE Trans. Geosci. Remote Sens. 47, 884-897.

ID: 59486
Title: Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS -2B.
Author: Chabitha Devaraj, Chintan A. Shah.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.13-24 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: CBERS, Orthorectification, Geometric correction, Geo-referencing, Image registration, Multi -temporal change
Abstract: China-Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a landsat data gap. Currently available multispectral images from the High Resolution CCD( Charged Coupled Device) camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified . The geometric accuracy of the HRCC multispectral image product is found to be within 2-11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper(TM) Level 1T (LIT) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images.
Location: TE 12 New Biology Building
Literature cited 1: Collins, S.H., 1968. Stereoscopic orthophoto maps. The Canadian,surveyor 22, 167-176.
Literature cited 2: : Ghoshtasby , A., 1998. Image registration by local approximation. Image vision comput.6, 255-261. Gianinetto, M., Scaioni, M., 2008. Automated geometric correction of high resolution pushbroom satellite data. Photogram . Eng. Rem.Sens.74, 107-116.

ID: 59485
Title: An improved dark object method to retrieve 500m - resolution AOT(Aerosol Optical Thickness ) image from MODIS data : A case study in the Pearl River Delta area, China.
Author: Lili Li, Jingxue Yang, Yunpeng Wang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol 89.1-12 (2014).
Subject: Photogrammetry and Remote Sensing
Keywords: Aersol, AOT retrieval, Dark object method , MODIS, Pearl River Delta
Abstract: This paper presents an improved Dark Dense Vegetation (DDV) method for retrieving 500 m -resolution aerosol optical depth (AOT) based on MOD04-C005 arithmetic with the Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration (NASA). The improvements include change of the movement pattern of retrieval window, selection of a more suitable aerosol type, and storage of the look-up table. The method is then applied to obtain the AOT over the Pearl River Delta region (PRD). By comparing the results with the co-temporal ground sunphotometer observations in 2010, the correlation coefficient is found to be 0.794 with RMSE 0.139 and their variations remain consistent. Contrasts between model values in 2008 and MODIS AOT products in the same date also reveal a high accuracy of the improved DDV method . We also performed sensitivity tests to analyze the impacts of several parameters on apparent reflectance at different bands, and the results show that apparent reflectance is much more sensitive to surface reflectance and AOT than to elevation.
Location: TE 12 New Biology Building
Literature cited 1: Carlson, T.N. et al., 1995. A new look at the simplified method for Remote-sensing of daily evapotranspiration. Remote Sens. Environ. 54(2), 161-167. Chu, D.A. et al., 2002. Validation of MODIS aerosol optical depth retrieval over land. Geophys. Res.Lett. 29 (12), 8007
Literature cited 2: Deng, X.J. et al., 2008. Long-term trend of visibility and its characterization in the Pearl River Delta (PRD) region , china. Atmos. Environ. 42(7), 1424-1435. Ferrare , R.A. et al., 1990. Satellite Measurements of large -scale air-pollution: measurements of Forest fire smoke . J. Geophys. Res.: Atmos.95 (D7), 9911-9925.

ID: 59484
Title: Detecting subcanopy invasive plant species in tropical rainforest by integating optical and microwave (InSAR)/PolinSAR) remote sensing data, and a decision tree algorithm.
Author: Abduwasit Ghulam, Ingrid Porton, Karen Freeman.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing vol. 88.174-192 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Betampona Nature Reserve, Guava, Madagascar Cardamom, Mollucca raspberry, Invasive plants, Remote sensing.
Abstract: In this paper, we propose a decision tree algorithm to characterize spatial extent and spectral features of invasive plant species (i.e., guava, Madagascar cardamom, and Molucca raspberry) in tropical rainforests by integrating datasets from passive and active remote sensing sensors. The decision tree algorithm is based on a number of input variables including matching score and infeasibility images from Mixture Tuned Mtched Filtering (MTMF), land -cover maps, tree height information derived from high resolution stereo imagery, polarimetric feature images, Radar Forest Degradation Index(RFDI), polarimetric and InSAR coherence and phase difference images. Spatial distributions of the study organisms are mapped using pixel -based winner-takes-All(WTA) algorithm,object oriented feature extraction, spectral unmixing, and compared with the newly developed decision tree approach. Our results show that the InSAR phase difference and polInSAR HH-VV coherence images of L-band PALSAR data are the most important variables following the MTMF outputs in mapping subcanopy invasive plant species in tropical rainforest. We also show that the three types of invasive plants alone occupy about 17.6% of the Betampona Nature Reserve (BNR) while mixed forest, shrubland and grassland areas are summed to 11.9% of the reserve. This work presents the first systematic attempt to evaluate forest degradation,habitat quality and invasive plant statistics in the BNR, and provides significant insights as to management strategies for the control of invasive plants and conversation in the reserve.
Location: TE 15 New Biology Building
Literature cited 1: Andrew, M.E., Ustin, S.L., 2008. The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens. Environ.112, 4301-4317. Armstrong, A.H., Shugart, H.H., Fatoyinbo, T.E., 2011. Characterization of community composition and forest structure in a madagascar lowland rainforest .Trop.Conserv.sci.4, 428-444.
Literature cited 2: 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, 1912-1926. Bajorski, P., Lentilucci, J., Schoot, J.R., 2004. Comparison of basis-vector selection methods for target and background subspaces as applied to subpixel target detection. In: Algorathims and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery x SPIE, Bellingham, Wash,pp. xiii, 674p.

ID: 59483
Title: Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning
Author: Peter J. Olsoy, Nancy F. Glenn, Patrick E. Clark, DeWayne R. Derryberry.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing 166-173 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Terrrestrial LiDAR, Sagebrush Steppe, Seasonal change, Fire, Great Basin .
Abstract: Sagebrush( Artemisia tridentata) , a dominant shrub species in the sagebrushsteppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and landuse change, Fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety- one sagebrush plants were scanned destructively sampled in the spring (n=46), while the other half were scanned again in the fall before destructive sampling (n=45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and 3D convex hull approach applied to TLS point cloud data. The 3D convex hull model estimated total and green biomass more accurately (R? =0.92 and R?=0.83, respectively ) than the voxel-based method (R?=0.86 and R?=0.73, respectively) . Seasonal differences in TLS-predicted green biomass were detected at two of the sites(p<0.001 and p=0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to ther dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward.
Location: TE 15 New Biology Building
Literature cited 1: Anderson, J.E., Inouye, R.S., 2001, Landscape-scale changes in plant species abundance and biodiversity of a sagebrush steppe over 45 years. Ecol. Monogr. 71 (4), 531-556. Baker, W.L., 2006.Fire and restoration of sagebrush ecosystems. Wildl. soc.Bull.34 (1), 177-185.
Literature cited 2: Barber, C.B., Dobkin, D.P., Huhdanpaa, H., 1996. The quickhull algorithm for convex hulls. ACM Trans. Math. softw. 22(4), 469-483. Bork, E.W., Su, J.G., 2007. Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sens. Environ.111(1), 11-24.