关键词: 遥感图像, 低秩表示, 分类

Abstract: With the rapid development of information technology and earth observation technology, remote sensing technology is playing an increasingly important role in our life and economic construction. The accuracy of class-ification model and its anti-jamming capability are crucial for the accurately mapping of complex land cover and land-use categories. This paper deals with the limitations of insufficient labeled samples of large-scale high-resolution remote sensing images. Based on the low-rank representation and semi-supervised learning methods, a large-scale remote sensing image classification method is proposed. In order to reduce the exponentially growing computational complexity of low-rank method, the image is divided into blocks and the low-rank representation is implemented for each block respectively. The results implemented on WorldView-2 remote sensing data show that the proposed method achieves relatively high classification accuracy with nearest neighbor classifier under few labeled samples. Therefore, the proposed remote sensing image classification method can effectively improve the accuracy of land cover classification, and has high efficiency in remote sensing image classification.

Key words: remote sensing image, low-rank representation, classification