Research of Remote Sensing Image Classification Technology Based on Multi-feature Combining and BoW Model

Abstract

Abstract: A new algorithm of image classification of multi feature combination and BoW model was proposed. SIFT, GIST, Census and Gabor color, and many other types of features were extracted from the images, and then through the experimental analysis to determine the best feature combination. According to the general K-means algorithm which did not consider the weight of each features, different feature component was put forward by using automatic weighted k-means algorithm, respectively SIFT, GIST, Gabor feature construct weights based on image features of vocabulary, using the soft coding algorithm for image coding, and using the SVM algorithm to complete the image classification. Experiments show that this method can effectively improve the classification accuracy of images.

Key words: multi-feature combining, k-means, bag of words, SVM

CLC Number:

P237.3

Li Ke, You Xiong, Du Lin. Research of Remote Sensing Image Classification Technology Based on Multi-feature Combining and BoW Model[J]. Journal of System Simulation, 2016, 28(6): 1386-1393.

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