| 1. |
- Ban, Yifang, et al.
(författare)
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Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping : object-based and knowledge-based approach
- 2010
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Ingår i: International Journal of Remote Sensing. - : Taylor & Francis. - 0143-1161 .- 1366-5901. ; 31:6, s. 1391-1410
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).
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| 2. |
- Ban, Yifang, et al.
(författare)
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Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection
- 2007
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Ingår i: Geoinformatics 2007Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9780819469120 ; , s. H7522-H7522
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Konferensbidrag (refereegranskat)abstract
- The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.
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| 3. |
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| 4. |
- Ban, Yifang, et al.
(författare)
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RADARSAT Fine-Beam SAR Data for Land-Cover Mapping and Change Detection in the Rural-Urban Fringe of the Greater Toronto Area
- 2007
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Ingår i: Proceedings, Urban Remote Sensing Joint Event, 2007.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for landuse/land-cover mapping and change detection in therural-urban fringe of the Greater Toronto Area (GTA). Five-date RADARSAT fine-beamSAR images were acquired during May to August in 2002. One scene of Landsat TM imagery was acquired in 1988 for change detection. The major landuse/land-coverclasses were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. Much better results were achieved with combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) and with raw images using object-based classification. The change detection procedure was able to identify the areas of significant changes, for example, major new roads, new low-density and high-density built up areas and golf courses, even though the overall accuracy of the change detection was rather low.
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| 5. |
- Hu, Hongtao, et al.
(författare)
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A Quantitative Characterization of Spatial Structure Features of Typical Urban Land Cover Types Using Morphological Method
- 2006
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Ingår i: IEEE International Geoscience and Remote Sensing Symposium. ; , s. 3714-3716
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The analysis of spatial structure features in high resolution imagery is one of the most important research fields in remote sensing information processing. In this study, we attempt to quantitatively describe the differences of some spatial structure features of the selected urban land cover(use) types in 0.61-metre resolution Quickbird panchromatic imagery using morphological method. Gray-scale granulometry based on opening of each selected land use type with discs of increasing size provided a size distribution which indicates the prevailing sizes of the image structures and the corresponding volume measurement (sum of grey levels of all image pixels) losses at these sizes which are normalized. A series of directional openings with linear structuring elements were applied for each land use type and the predominant orientation of the land use type Was obtained by selecting the direction in which the volume measurement was maximal when opening was used. Also the strength of the predominant orientation information was obtained. Clear distinctions among different kinds of selected urban land use types were presented through the previously obtained prevailing sizes, normalized volume losses at these sizes, predominant orientation and its strength. It shows that these variables which reflect the size and orientation information could be integrated to characterize spatial structure features of urban land use types, which can be used in the extraction of some land use types from high resolution images.
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| 6. |
- Hu, Hongtao, et al.
(författare)
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Multitemporal RADARSAT-2 ultra-fine beam SAR data for urban land cover classification
- 2012
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Ingår i: Canadian journal of remote sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 38:1, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- High-resolution optical satellite images have been widely used to update land cover information and monitor changes in urban areas. Several spaceborne synthetic aperture radar (SAR) systems are now providing SAR imagery with a spatial resolution comparable to high-resolution optical systems. Although SAR data is more reliably available than optical data, it takes more effort to employ high-resolution SAR imagery for urban applications owing to the difficulty in interpreting the complex content in SAR imagery over urban areas. The objective of this research was to develop effective object-based and rule-based methods for classification of high-resolution SAR imagery over urban areas. Multitemporal RADARSAT-2 ultra-fine beam C-HH SAR images with a pixel spacing of 1.56 m were acquired over the north part of the Greater Toronto Area during June to September in 2008. The SAR images were preprocessed and then segmented by means of a multiresolution segmentation algorithm. A range of spectral, geometrical, and textural features were selected and calculated for image objects. The image objects were classified based on these features using support vector machines (SVM). Compared with the nearest neighbor classifier, the object-based SVM produced much higher urban land cover classification accuracy (Kappa 0.43 vs. 0.63). The SVM classification result was then improved by developing specific rules to resolve the confusion among some classes. The final result indicated that the proposed methods could achieve a satisfactory overall accuracy (81.8%) for urban land cover classification using very high-resolution RADARSAT-2 SAR imagery.
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| 7. |
- Hu, Hongtao, et al.
(författare)
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Segmentation of High-resolution Multi-spectral Image of Urban Areas Based on Extended Morphological Profiles
- 2007
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Ingår i: 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8. - : IEEE. - 9780780395091 ; , s. 3716-3719
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Konferensbidrag (refereegranskat)abstract
- High-resolution multi-spectral remote sensing image of urban areas provides both structural and spectral information about urban scenes. In segmentation of such complex image scenes, very thin, enveloped or nested regions may have to be retained. Standard morphological segmentation approaches which are based on edge-detection do, not perform well for such scenes. In this study, segmentation of such images based on extended morphological profiles is proposed. First, fundamental morphological vector operations (erosion and dilation) are defined by extension, taking into account the spatial and spectral information in simultaneous fashion. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profiles, which is constructed based on the repeated use of openings and closings by reconstruction with a structuring element (SE) of increasing size. Then, the morphological multi-scale characteristic of the image at each pixel is defined as the SE size with the greatest associated value in the corresponding derivative of the extended morphological profiles. The multi-scale segmentation derived from the morphological multi-scale characteristic could not be the final segmentation result because of over- or under- segmentation in local parts of the image. Therefore, appropriate post-processing is used to process the previous multi-scale segmentation to gain more accurate segmentation result. The proposed approach is applied to high-resolution multi-spectral QUICKBIRD imagery of urban areas. The experiment result demonstrates good performance of this approach.
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| 8. |
- Hu, Hongtao, et al.
(författare)
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Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban Areas
- 2014
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Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - 1939-1404 .- 2151-1535. ; 7:8, s. 3248-3261
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Tidskriftsartikel (refereegranskat)abstract
- Unsupervised change detection in multitemporal single-polarization synthetic aperture radar (SAR) images often involves thresholding of the image change indicator. If one class, which is usually the unchanged class, comprises a disproportionately large part of the scene, the image change indicator may have a unimodal histogram. Image thresholding of such a change indicator is a challenging task. In this paper, we present an automatic and effective approach to the thresholding of the log-ratio change indicator whose histogram may have one mode or more than one mode. A bimodality test is performed to determine whether the histogram of the log-ratio image is unimodal or not. If it has more than one mode, the generalized Kittler and Illingworth thresholding (GKIT) algorithm based on the generalized Gaussian model (GG-GKIT) is used to detect the optimal threshold values. If it is unimodal, the log-ratio image is divided into small regions and a multiscale region selection process is carried out to select regions which are a balanced mixture of unchanged and changed classes. The selected regions are combined to generate a new histogram. The optimal threshold value obtained from the new histogram is then used to separate unchanged pixels from changed pixels in the log-ratio image. Experimental results obtained on multitemporal SAR images of Toronto and Beijing demonstrate the effectiveness of the proposed approach.
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| 9. |
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| 10. |
- Hu, Hongtao, 1980-
(författare)
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Urban Land-cover Mapping with High-resolution Spaceborne SAR Data
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space. This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008. SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results. The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images.
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