On unsupervised segmentation of remotely sensed imagery using nonlinear regression
作者:
S. T. ACTON,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1996)
卷期:
Volume 17,
issue 7
页码: 1407-1415
ISSN:0143-1161
年代: 1996
DOI:10.1080/01431169608948712
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A novel segmentation technique for remotely sensed imagery is introduced. Here, image segmentation is posed as a regression problem. The solution is computed by generating a piecewise constant image with minimum deviation from the original input image. The regression technique avoids the problems of region merging, poor boundary localization, region boundary ambiguity, region fragmentation, and sensitivity to noise. Results generated from the nonlinear regression technique and from other traditional segmentation algorithms are given for a study of the Great Victoria Desert using Landsat Thematic Mapper (TM) imagery.
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