Abstract
Land cover information extraction using object-based image classification has become a widely accepted trend in remote sensing. Segmented objects have abundant features including spectrum, shape and texture, and feature selection affects the precision and efficiency of classification. However, how to select less features while achieving high classification accuracy remains unsolved. In this paper, an object-based image classification method based on Support Vector Machine (SVM) combined with Particle Swarm Optimization (PSO)-based feature selection evolutionary algorithm is proposed to evaluate its potential on land cover classification. The experiments results using Tiangong-2 Wide-band images demonstrate that the proposed SVM classification combined with PSO for low-middle resolution images can yield a preferable classification outcome especially for grassland, water and forest with the user’s accuracy higher than 90%.
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Thanks to China Manned Space Engineering for providing space science and application data products of Tiangong-2.
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Qiao, H., Wan, X. (2019). Object-Based Classification from Tiangong-2 Using Support Vector Machine Optimized with Evolutionary Algorithm. In: Gu, Y., Gao, M., Zhao, G. (eds) Proceedings of the Tiangong-2 Remote Sensing Application Conference. Lecture Notes in Electrical Engineering, vol 541. Springer, Singapore. https://doi.org/10.1007/978-981-13-3501-3_21
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DOI: https://doi.org/10.1007/978-981-13-3501-3_21
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