This paper has explored the method and scheme for classifying different kinds of agricultural land from the high resolution remote sensing images by using multi-scale segmentation technology and rule-based image analysis approaches. Firstly, optimal segmentation scale was examined to construct a multi-scale segmentation level network according to the size of objects. Secondly, on the basis of spectrum, shape, texture and topology characteristics of images, several features of NDVI, shape indices, brightness, mean spectral value of red band, and ratio of near-infrared band, the standard deviation of near-infrared band and homogeneity were selected to classify objects into four agricultural land categories. The results show that these characteristics are effective in identifying agricultural land type and that the precision is higher than that of the traditional maximum likelihood classification.
邓媛媛, 巫兆聪, 易俐娜, 胡忠文, 龚正娟. 面向对象的高分辨率影像农用地分类[J]. 国土资源遥感, 2010, 22(4): 117-121.
DENG Yuan-Yuan, WU Zhao-Cong, YI Li-Na, HU Zhong-Wen, GONG Zheng-Juan. Research on Object-oriented Classification of Agricultural Land Based on High Resolution Images. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(4): 117-121.
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