Abstract
As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other, the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes. In order to solve this problem, make better fusion of different modal data and the relationship between the said features, this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration, by training the SVM model. Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features. Compared with other methods, the fusion method can achieve higher classification accuracy and better classification effect.
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Yu, X., Dong, H., Mu, Z., Sun, Y. (2020). Classification of Remote Sensing Images Based on Band Selection and Multi-mode Feature Fusion. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_45
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DOI: https://doi.org/10.1007/978-981-15-7981-3_45
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