Knowledge-based segmentation of Landsat images
Knowledge-based segmentation of Landsat images
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DOI：
10.1109/36.73663
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发表时间：
1991-03
期刊：
影响因子：
--
通讯作者：
J. Ton;J. Sticklen;Anil K. Jain
中科院分区：
文献类型：
--
作者：
J. Ton;J. Sticklen;Anil K. Jain
提出了一种基于知识的陆地卫星图像分割方法。通过从输入图像中提取核信息来提供图像的初始解译，并通过使用基于知识的分层分类器来区分研究区内的主要土地覆盖类型来解决图像分割问题。该方法可以在没有任何先验图像相关信息的情况下对陆地卫星图像进行分割和解译。从训练后的土地覆盖数据中构建一般的光谱土地覆盖知识，并通过道路检测程序获得图像的道路信息。>；
A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program. >