Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11323
Title: Classification of very high spatial resolution imagery based on a new pixel shape feature set
Authors: Zhang, H
Shi, W 
Wang, Y
Hao, M
Miao, Z
Keywords: Classification
High spatial resolution multispectral imagery (HSRMI)
Pixel shape feature set (PSFS)
Spatial feature extraction
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE Geoscience and remote sensing letters, 2014, v. 11, no. 5, 6627928, p. 940-944 How to cite?
Journal: IEEE Geoscience and Remote Sensing Letters 
Abstract: This letter presents a novel spatial features extraction method for the high spatial resolution multispectral imagery (HSRMI) classification. First, Canny filter algorithm is applied to extract the edge information to obtain the fuzzy edge map. Secondly, adaptive threshold value for each pixel's homogeneous region (PHR) calculation is determined based on the fuzzy edge map and original image. Next, the PHR for every pixel is obtained based on the fuzzy edge map, adaptive threshold value and original image. And then, the pixel shape feature set (PSFS) is extracted based on the PHR. Lastly, SVM classifier is applied to classify the hybrid spectral and PSFS. Two different experiments were performed to evaluate the performance of PSFS, in comparison with spectral, gray level co-occurrence matrix (GLCM) and the existing pixel shape index (PSI). Experimental results indicate that the PSFS achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.
URI: http://hdl.handle.net/10397/11323
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2282469
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