Remote sensing image classification algorithm based on mRMR selection and IFCM clustering

doi:10.13474/j.cnki.11-2246.2019.0108

Abstract

Abstract:

In order to solve the problem of poor classification precision caused by high correlation redundancy between features of high-resolution image and poor robustness of FCM clustering, an image classification algorithm based on mRMR selection and IFCM clustering is proposed. First, the image segmentation is carried out based on the object confidence index (OC), then the feature selection is realized by mRMR algorithm to solve the feature redundancy problem. The extracted feature is put in classifier and final classification result is clustered by IFCM algorithm. Comparison of experimental results show that the proposed algorithm can reduce feature correlation and redundancy and effectively improve image classification accuracy.

Key words: redundancy, mRMR selection, IFCM clustering, OC, image classification

CLC Number:

P237

HUANG Lei, XIANG Zejun, CHU Heng. Remote sensing image classification algorithm based on mRMR selection and IFCM clustering[J]. 测绘通报, 2019, 0(4): 32-37.

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