Chu S, Hong L, Liu C, et al. A new regional shape index for classification of high resolution remote sensing images[C]//2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2014: 156-160.[DOI：10.1109/EORSA.2014.6927869]
发布时间：2018-04-26
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摘要：Based on the object-oriented method, this paper presents a new regional shape index (RSI). RSI is a regional feature which measures the gray similarity distance within region in every direction. Firstly, the original image is segmented to obtain small regions. Then the center in each region is calculated, and the distance is calculated from the center of each region within image to boundary of each region in every direction. Finally, the results by RSI are compared with some textural features extracted using Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Regional Gray Level Co-occurrence Matrix (R-GLCM), Regional Local Binary Patterns (R-LBP). Experiments are conducted on high spatial resolution remote sensing image of Washington DC obtained by HYDICE and texture synthesis image confirm that the proposed method is feasible and effective. These experiments demonstrate the classification approach based on RSI feature results in higher classification accuracy than other methods. In a word, classification approaches based the regional level feature results, such as RSI, R-GLCM and R-LBP in higher classification accuracy than those approaches that consider pixel-wise feature, such as GLCM and LBP.
页面范围：156-160
是否译文：否
发布期刊链接：https://ieeexplore.ieee.org/abstract/document/6927869
上一条： Liu C, Hong L, Chu S, et al. A SVM ensemble approach combining pixel-based and object-based features for the classification of high resolution remotely sensed imagery[C]//2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2014: 140-144.
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