Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 437-441, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-437-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 437-441, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-437-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 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 Xuemei Zhao et al.
  • 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.