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.
ID: 59467
Title: CPU/ GPU near real-time preprocessing for ZY- 3 Satellite images: Relative Radiometric correction, MTF compension , and geocorrection
Author: Liuyang Fang, Mi Wang , Daren Li, jun pan
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: ZY-3 image, Preprocessing, CPU/GPU, Optimization, Workload distribution
Abstract: ZY-3 is the first high -acurracy civil stereo-mapping optical satellite of china. It greately improves china ' s optical sattelite image resolution with a boom in data volume , calling for new challenges in processing real-time applications. on the other hand, using central processing unit ( CPU) / graphic processing unit (GPU) to resolve data -internsive remote sensing problems becomes a hot issue . In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 sattelite images, focusing on three key processors : relative radiometric correction (RRC), modulation transfer function compensation (MTFC), and geocorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore , to fully exploit the CPU ' s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84- fold speedup ratio in ZY-3 nadir image preprocessing ( the corresponding run time is 11.6o s for one image ), which is capable of meeting the requirement of near real - time response to the applications that follow . In addition, with the supportability of IEEE 754-2008 float ing - point standard in the fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of the image preprocess as done traditionally with CPU processors.
Location: TE 15 New Biology Building
Literature cited 1: Javier, R.S., Maria, C.C., Julio, M.H., 2012, Geocorrection for airborne pushbroom imagers . IEEE Trans. Geosci. Remote Sens. 50 (11), 4409-4419.
Literature cited 2: Karimi, K., Dickson N.G., Hamze, F., 2010. A Performance comparison of CUDA and open CL. < http
Karimi K ., Dickson N.G., Hamze, F., 2010. A performance comparision of CUDA and Open CL <htpp://arxiv.org /pdf / 1005.2581>.
ID: 59466
Title: A practical method for SRTM DEM correction over vegetated
Author: Yanjun Su, Qinghua Guo
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: Digital elevation model(DEM) , Shuttle RadarTopographic Mission(SRTM), light Detection and Ranging (Lidar), Correction, Vegetation, Mountain
Abstract: Digital elevation models(DEMs) are essential to various applications in topography ,geomorphology, hydrology, and ecology. The shuttle Radar Topographic Mission(SRTM) DEM data set is one of the most complete and most widely used DEM data sets; it provides accurate information on elevations over bare land areas . However, the accuracy of SRTM data over vegetated mountain areas is relatively low as a result of the high relief and penetration limitation of the c-band used for obtaining global DEM products. The objective of this study is to assess the performance of SRTM DEMs and correct them over vegetated mountain areas with small-footprint airborne Light Detection and Ranging (Lidar) data ,which can develop elevation products and vegetation products[e.g.,vegetation height,Leaf Area Index(LAI)] of high accuracy. The assesing results show that SRTM elevations are systematically higher than those of hte actual land surfaces over vegetated mountain areas. The mean difference between SRTM DEM and Lidar DEM increases with vegetation height , Whereas the standard deviation of the difference increases with slope.. To improve the accuracy of SRTM DEM over vegetated mountain areas , a regression model between the SRTM elevation bias and vegetation height, LAI, and slope was developed based on one control site. Without changing any coefficients, this model was proved to be applicable in all the nine study sites, which have various topography and vegetation conditions. The mean bias of the corrected SRTM DEM at the nine study sites using this model (absolute value) is 89% smaller than that of the original SRTM DEM, and the standard deviation of the corrected SRTM elevation bias is 11% smaller.
Location: TE 15 New Biology Building
Literature cited 1: Abshire, J.B., SUN, X. L., Riris, Sirota, J.M., Mc Garry, J.F., Palm, S., Yi., D.H., Liiva., P., 2005. Geoscience Laser altimeter system (GLAS) on the ICES at mission: on orbit measurement performance. Geophys. Res. Lett. 32 (21), L2 1 S02.
Literature cited 2: Berthier, E., Arnaud, Y., Vincent, C., Remy, F., 2006. B iases of SRTM in high- mountain areas : implications for the monitoring of glacier volume changes . Geophys. Res. Lett. 33 (8), L08502
ID: 59465
Title: Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using worldview-2 imagery
Author: Mariana Belgiu, Lucian Dragut, Josef Strobl
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 205-215 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Land cover, Comparison, Image,Accuracy,Urban,Experiment,OBIA
Abstract: The increasing availability of high resolution imagery has triggered the need for atomated image analysis techniques, with reduced human intervention and reproducible analysis procedures.The knowledge gained in the past might be of use to achieving this goal , if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assiggned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactorily results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.
Location: TE 15 New Biology Building
Literature cited 1: Agresti, A., 1996. An introduction to categorical Data Analysis.John Wiley & sons, New York.
Literature cited 2: Albrecht, F., Lang, S., Holbling, D., 2010. Spatial accuracy assesment of object boundaries for object-based image analysis. Int.Arch. Photogram. Remote Sens.Spatial Inf.Sci. 38 (4)
ID: 59464
Title: Geomorphometric pattern recognition of SRTM data applied to the tectonic interpretation of the Amazonian landscape
Author: Delano Menecucci Ibanez, Fernando Pellon de Miranda, Claudio Riccomini
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 192-204 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Amazon, SRTM, Geomorphometry,Fractal geometry
Abstract: The Amazon landscape spatial variabilioty and anisotropic terends in the uatuma and urubu River regions are evaluated using geomorphometric techniques such as fractal dimension, drainage network density and semivariogram.These procedures were applied to the digital elevation model(DEM) of the shuttle Rdar Topography Mission(SRTM). This evaluation facilitated the definition of geomorphometric domains with different degrees of roughness(fractal dimension) and elevation (semivariogram).These ares are consistent with known qualitative relief types. Furthermore, known geological structures in the subsurface and surface apparently influence the spatial variability of thesegeomorphometric variables. This is the silves area case, where the hilly topography exhibits several annular and radial rivers, denoting subsurface control due to faults and folds that were mapped by seismic surveys. Another possible influence example is the spatial coincidence between structures mapped by magnetic data with low dissection zones delineated as result of drainage network density analysis.In addition to the spatial distribution, the anisotropic trends of these geomorphometric variables were analyzed and compared with geological and geophysicalinformation. Results indicate that the predominant directions for topographic semivariance anisotropy are NNE-SSW and NE-SW for the interfluvial regions, as well as NW-SE for the alluvial plains. The highest agglomeration direction in the drainage network,as shown by its anisotropy, coincides with the studied region ' s currentmaximum horizontal stress direction, except in the flood plains. The direction with the most pronounced roughness ,NNE-SSW, coincides with the direction of waterfalls and rapids. This study demonstrates that spatial variability knowledge and anisotropic trends of geomorphometric parameters is useful to understand ther geology and geomorphology of hte central Amazon region.
Location: TE 15 New Biology Building
Literature cited 1: Almeida-Filho,R., Mirranda,F.P.,2007.Mega capture of the Rio Negro and information of the Anavilhanas archipelago, central Amazonia, Brazil:evidence in an SRTM digital elevation model.Remote sens.Environ.110(3),387-392.
Literature cited 2: Assumpcao, M., 1992.The regional intraplate stress field in south America.J. Geophys.Res.97(B8), 11.889-11.903.
ID: 59463
Title: Geographic Object-Based Image Analysis - Towards a new paradigm.
Author: Thomas Blaschke, Geoffrey J. Hay, Maggi Kelly, Stefan Lang, Peter Hofmann, Elisabeth Addink, Raul Queiroz Feitosa, Freek van der Meer, Harald van der Werff, Frieke van Coillie, Dirk Tiede
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 180-191 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: GEOBIA; OBIA; GIScience; Remote sensing; Image segmentation; Image classification.
Abstract: The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59462
Title: Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping.
Author: Luca Demarchi, Frank Canters, , Claude Cariou, Giorgio Licciardi , Jonathan Cheung-Wai Chan,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 166-179 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Airborne high-resolution hyperspectral imagery; APEX; Data dimensionality reduction; BandClust; Autoassociative Neural Network; Machine learning classifiers.
Abstract: Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method - the first a transformation based approach, the second a feature-selection based approach - for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method ' s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59461
Title: Contextual classification of lidar data and building object detection in urban areas.
Author: Joachim Niemeyer, Franz Rottensteiner, , Uwe Soergel,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 152-165 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: LIDAR; Point cloud; Classification; Urban; Contextual; Building; Detection.
Abstract: In this work we address the task of the contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. It is a flexible approach for obtaining a reliable classification result even in complex urban scenes. In this way, we benefit from the consideration of context on the one hand and from the opportunity to use a large amount of features on the other hand. Considering the interactions in our experiments increases the overall accuracy by 2%, though a larger improvement becomes apparent in the completeness and correctness of some of the seven classes discerned in our experiments. We compare the Random Forest approach to linear models for the computation of unary and pairwise potentials of the CRF, and investigate the relevance of different features for the LiDAR points as well as for the interaction of neighbouring points. In a second step, building objects are detected based on the classified point cloud. For that purpose, the CRF probabilities for the classes are plugged into a Markov Random Field as unary potentials, in which the pairwise potentials are based on a Potts model. The 2D binary building object masks are extracted and evaluated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction. The evaluation shows that the main buildings (larger than 50 m2) can be detected very reliably with a correctness larger than 96% and a completeness of 100%.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59460
Title: Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea).
Author: Tingwei Cui, Jie Zhang, Junwu Tang, Shubha Sathyendranath, Steve Groom, Yi Ma, Wei Zhao, Qingjun Song
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 137-151 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Oceanography; Comparison; Retrieval; Algorithm; Satellite; Accuracy; Optical.
Abstract: The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(Lamda) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15-27% and root-mean-square-error (RMS) of 0.0021-0.0039 sr?1, whereas the Rrs(Lamda) uncertainty at 412 nm is the highest (APDm 47-62%, RMS 0.0027-0.0041 sr?1). The uncertainties of the aerosol optical thickness (AOT) ?a, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20-30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59459
Title: Hierarchical extraction of landslides from multiresolution remotely sensed optical images
Author: Camille Kurtz, Andr? Stumpf , Jean-Philippe Malet, Pierre Gan?arski, Anne Puissant, Nicolas Passat,
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 122-136 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Landslide mapping; VHR images; Multiresolution region-based analysis; Hierarchical approach; Binary partition tree; Domain adaptation.
Abstract: The automated detection and mapping of landslides from Very High Resolution (VHR) images present several challenges related to the heterogeneity of landslide sizes, shapes and soil surface characteristics. However, a common geomorphological characteristic of landslides is to be organized with a series of embedded and scaled features. These properties motivated the use of a multiresolution image analysis approach for their detection. In this work, we propose a hybrid segmentation/classification region-based method, devoted to this specific issue. The method, which uses images of the same area at various spatial resolutions (Medium to Very High Resolution), relies on a recently introduced top-down hierarchical framework. In the specific context of landslide analysis, two main novelties are introduced to enrich this framework. The first novelty consists of using non-spectral information, obtained from Digital Terrain Model (DTM), as a priori knowledge for the guidance of the segmentation/classification process. The second novelty consists of using a new domain adaptation strategy, that allows to reduce the expert ' s interaction when handling large image datasets. Experiments performed on satellite images acquired over terrains affected by landslides demonstrate the efficiency of the proposed method with different hierarchical levels of detail addressing various operational needs.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59458
Title: Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)
Author: Chandi Witharana, Daniel L. Civco.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 108-121 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Multiresolution segmentation, GEOBIA, Opmital scale, Image objects, Empirical methods.
Abstract: Multiresolution segmentation (MRS) has proven to be one of the most successful image segmentation algorithms in the geographic object-based image analysis (GEOBIA) framework. This algorithm is relatively complex and user-dependent; scale, shape, and compactness are the main parameters available to users for controlling the algorithm. Plurality of segmentation results is common because each parameter may take a range of values within its parameter space or different combinations of values among parameters. Finding optimal parameter values through a trial-and-error process is commonly practiced at the expense of time and labor, thus, several alternative supervised and unsupervised methods for supervised automatic parameter setting have been proposed and tested. In the case of supervised empirical assessments, discrepancy measures are employed for computing measures of dissimilarity between a reference polygon and an image object candidate. Evidently the reliability of the optimal-parameter prediction heavily relies on the sensitivity of the segmentation quality metric. The idea behind pursuing optimal parameter setting is that, for instance, a given scale setting provides image object candidates different from the other scale setting; thus, by design the supervised quality metric should capture this difference. In this exploratory study, we selected the Euclidean distance 2 (ED2) metric, a recently proposed supervised metric, whose main design goal is to optimize the geometrical discrepancy (potential segmentation error (PSE)) and arithmetic discrepancy between image objects and reference polygons (number-of segmentation ratio (NSR)) in two dimensional Euclidean space, as a candidate to investigate the validity and efficacy of empirical discrepancy measures for finding the optimal scale parameter setting of the MRS algorithm. We chose test image scenes from four different space-borne sensors with varying spatial resolutions and scene contents and systematically segmented them using the MRS algorithm at a series of parameter settings. The discriminative capacity of the ED2 metric across different scales groups was tested using non-parametric statistical methods. Our results showed that the ED2 metric significantly discriminates the quality of image object candidates at smaller scale values but it loses the sensitivity at larger scale values. This questions the meaningfulness of the ED2 metric in the MRS algorithm ' s parameter optimization. Our expense of time. In this respect, especially in operational-level image processing, it is worth to re-think the trade-off between execution time of the processor-intensive MRS algorithm at series of parameter settings targeting a less-sensitive quality metric and an expert-lead trial-and-error approach.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59457
Title: Landscape Dynamics.
Author: T V Ramachandra
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 93-107 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: MLS data, Curb, Road markings, Intensity, Multi-thershold segmentation, Morphological operation.
Abstract: A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59456
Title: Using mobile laser scanning data for automated extraction of road markings.
Author: Haiyan Guan, Jonathan Li, Yongtao Yu, Cheng Wang, Michael Chapman, Bisheng Yang.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 93-107 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: MLS data, Curb, Road markings, Intensity, Multi-thershold segmentation, Morphological operation.
Abstract: A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None