Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 437-441, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/437/2016/
doi:10.5194/isprs-archives-XLI-B7-437-2016
 
21 Jun 2016
AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES
Xuemei Zhao, Yu Li, Quanhua Zhao, and Chunyan Wang Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin, Liaoning, 123000, China
Keywords: entropy, KL information, spiltting/merging, number of classes, image segmentation, fuzzy clustering Abstract. different classes, to estimate the number of classes in image segmentation issues. In this strategy, the information of a homogeneous region is measured by entropy. Then a region is considered to be disordered and should be split if its entropy is more than a given threshold. On the contrary, when the KL information of two homogeneous regions is less than a threshold, it is believed that they are similar and should be merged. The entropy-KL strategy can be combined with any kind of segmentation algorithm since it uses the information and distance as a general way to decide the number of classes. In this paper, the HMRF-FCM algorithm is employed as the segmentation process and combined with the entropy-KL strategy to induce a segmentation algorithm which can fix the number of classes automatically. The proposed algorithm is performed on synthetic image, real panchromatic images and SAR images to demonstrate the effectiveness.
Conference paper (PDF, 1777 KB)


Citation: Zhao, X., Li, Y., Zhao, Q., and Wang, C.: AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 437-441, doi:10.5194/isprs-archives-XLI-B7-437-2016, 2016.

BibTeX EndNote Reference Manager XML