文章基本信息

标题：Combination of Textural Features for the Improvement of Terrain Classification and Change Detection
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作者：Hoang Lam Le ; Dong-Min Woo
期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2015
卷号：9
期号：5
页码：145-154
DOI：10.14257/ijseia.2015.9.5.14
出版社：SERSC
摘要：Good segmentation of satellite images plays a significant role in monitoring of urban areas, as well as of natural terrain. The analysis of two segmented observations can provide new information relating to land use, shifting cultivation, deforestation, or environmental changes. This paper introduces a combination of textural features that can achieve very good results for terrain segmentation. We implement BPNN (Back Propagation neural network) and Adaboost algorithms for the classification of an urban area in terms of a combination of several textural features. Using high resolution IKONOS satellite images of the Boston area, we carry out experiments on terrain classification. Experimental results show that a combination of co-occurrence and Harr-like features can be used to obtain high accuracy of terrain classification of 89.8-94.5% with the Adaboost classifier; this new method outperforms other implementations. To verify the efficiency of the proposed classification method, change detection using temporal images is also tested via experiment. The resulting change map shows that a newly developed area can be successfully detected.
关键词：segmentation; satellite image; textural feature; terrain classification; change detection