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Habitation Extraction and Edge Optimization

Author: ZhangYiSheng
Tutor: LiuZhi
School: PLA Information Engineering University
Course: Photogrammetry and Remote Sensing
Keywords: Residential Information Feature Extraction Texture analysis Self-organizing feature neural network Limited statistical threshold segmentation characteristics Mathematical Morphology
CLC: P237
Type: Master's thesis
Year: 2009
Downloads: 103
Quote: 0
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Automatically extract residential areas from panchromatic image and edge optimization in surveying and mapping production can resolve surface features to identify , extract labor intensive , tedious work difficult ; terrain classification , image analysis , target identification etc. plays a very important role in many applications . This paper focuses on the extraction and the edge of the land of residents optimization do the following work : 1, by introducing the mean gray , gray variance the gradation variation frequency of the x - direction and y - direction , the number of particles , the particles mean area six limited threshold segmentation STATS characteristics. Statistical characteristics of a limited threshold for small and medium- scale remote sensing image texture segmentation method . 2 , the use of self- organizing feature map neural network (SOM) method , the different threshold segmentation statistical features as a parameter input , and the establishment of the neural network model to extract residential areas . Residents residents in need extract containing image clustering segmentation map spots . Mapping integrated requirements as well as the proposed algorithm characteristics , based on changing the image extracted texture features of the threshold interval and clustering step of the formulation of the five rules : go holes , take the given area threshold , the elimination of the four corners of a like element of the convex out , filling a pixel depression filled on the edge of a pixel depression straightened n (n is determined by the image resolution and scan resolution ) as $ sunken or protruding edges to improve residents Map spots . This optimization method and the method of mathematical morphology . This established residential area extraction methods do not block residential area given seed point Once the neural network model training can be a number of the same period homologous image processing automatically without the need for training of each image . Which can be convenient , fast and efficient extraction of the residential areas , largely to reduce the workload of manual intervention . This method of extraction of the residents of the border is more structured, conducive to optimize . The edge optimization rules and methods developed can get a good residential area outside contour .

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CLC: > Astronomy,Earth Sciences > Surveying and Mapping > Photogrammetry and Surveying, Mapping and Remote Sensing > Surveying, Mapping and Remote Sensing technology
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