Automatic classification of GF-2 remote sensing imagery based on active learning and bag of visual words model

doi:10.13474/j.cnki.11-2246.2019.0053

Abstract

Abstract:

The improvement of high-resolution satellite images makes the spectrum and texture more rich and complex, which poses challenges for the automatic classification. Therefore, this paper combines active learning and bag of word model for image classifications. First, a multi-scale segmentation is implemented to generate image objects. Second, bag of word model is used to establish the semantic feature of image object. Finally, the uncertainty sample distribution is well considered, and the optimal samples are selected iteratively for training SVM to classify image. To verify the effectiveness and robustness, the high-resolution image in Shandong province was used as experimental data. The results show that the proposed method can effectively classified the study area into four types:water, ground, vegetation, and building, with the overall accuracy of over 90.6%.

Key words: remote sensing imagery, machine learning, classification, active learning

CLC Number:

P237

ZHANG Jinying, YAO Guanghu, LIN Lin, GUO Huaixuan. Automatic classification of GF-2 remote sensing imagery based on active learning and bag of visual words model[J]. 测绘通报, 2019, 0(2): 103-107.

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