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Study On Object-based Classification Of High-resolution Remote Sensing Imagery

Posted on:2011-12-08Degree:DoctorType:Dissertation
Country:ChinaCandidate:J ChenFull Text:PDF
GTID:1118330335489005Subject:Cartography and Geographic Information Engineering
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Great achievements of remote sensing technology having extended the visual field of Earth Observation, makes human obtain very abundant geographical information. With the progresses in spatial resolution of satellite sensors, high-resolution remotely sensed imagery has play an important role in regional studies of urban planning, territorial resources management, geological survey, vehicle detection, and concerned application fields. Though the technology of the remote sensing image classification has made considerable progress, available investigations have shown that the pixel-based approach has explicit limits in classification of such high-resolution remotely sensed imagery. For overcoming the drawbacks suffered by conventional information extraction method which based on pixel, a new method named Object-based Image Analysis has emerged as a reflection of the times. As a sub-discipline of GIScience, OBIA represents a significant trend in remote sensing and GIScience. The main contents and results of the study centred on OBIA were presented as follows.(1) An efficient multiscale approach based on watershed transform and wavelet transform is presented for segmentation of the pan-sharpened high-resolution multispectral remote sensing imagery. The procedure toward complete segmentation using the proposed method consists of pyramid representation, image segmentation, region merging and result projection. Multi-scale gradient images are obtained by applying phase congruency model to approximation coefficients, and gradient magnitudes of all bands are fused at each scale. The optimal scale of wavelet decomposition is chosen by analysis local gradient variance varying correspond to different scales and varieties of geo-objects. Multi-level marker location algorithm is subsequently used to locate significative regions that are homogeneous in terms of texture and intensity, by moving threshold and extended minima transform. A multi-constraint region merging strategy considering spatial adjacency relation, area, spectral and textural properties is proposed to merge the initial segments. Pixels at boundaries are assigned to refine object contours. The experimental results demonstrate that the developed method can be applied to the segmentation of high resolution remote sensing images and get the high accuracy segmentation.(2) An approach based on texture frequency analysis is proposed to determine the optimal spatial scale for high resolution imagery. Four typical geo-objects are used to analyze their frequency properties of the response to the Fourier transform domain. The original image is up-scaled to different spatial resolutions using point spread function. The adequate spatial scale is chosen from the up-scaled images according to the change patterns in the radius distribution and angle distribution curves. Separability among four types of objects at six scales is analyzed based on texture feature to approve the feasibility of the new method. Object-oriented classification of the panchromatic image by means of SVM is implemented, and results of experiment demonstrate that higher accuracy can be obtained at the optimal spatial scale.(3) An object-based algorithm based on rough set theory is proposed to classify different objects extracted from high-resolution remotely sensed imagery. The indiscernibility relation, upper or lower approximation, and knowledge reduction in the rough set theory are used to discover the connotative rules of classification from the Gabor texture feature. Based on the preliminary clustering result derived from spectral feature of objects, the ultimate classification is achieved referring to the rules. A new technique to discretize continuous interval-valued attributes is developed, which is very suitable for the object-based classification. The experiments demonstrate that the proposed method can achieve better results and better accuracies.(4) A new object-based method for classification of high-resolution remotely sensed imagery is proposed in the paper, which integrates support vector machine (SVM) technique with rough-set-based granular computing (RSBGC). Spectral characteristic is got from multi-spectral data and texture feature is extracted by Gabor filters. Multi-kernel SVM is used to present preparatory object-based classification, and information granularities are obtained through intersection of the classification results. Granularities are differentiated by means of comparing the Euclidean distance between average value of granularity and every sample central moment. Spatial adjacency relation among the granularities is quantitative analyzed in order to classify the uncertain granularities after the former clustering. The resulting classification is achieved by little artificial interaction identification. A comparative experiment is performed with both SVM and neural network methods based on RBF-kernel function. It is shown that the proposed method can obtain better classification results.Finally, after concluding all about research work in this dissertation, further work need be advanced study:1) the integration of multi-method and the multi-angle analysis will be conducive to improve the segmentation; 2) scale factors in remote sensing and scale requirement according the specific application should be considered together; 3) how to give full play to the advantages of intelligent methods in remote sensing information extraction.
Keywords/Search Tags:watershed transform, phase congruency, multi-scale segmentation, choosing of optimal spatial scale, object-based classification
PDF Full Text Request
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