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31 January 2019 Efficient local-region approach for high-resolution remote-sensing image retrieval and classification
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Abstract
A local-region approach based on bag-of-visual-words model for high-resolution satellite image (HRSI) retrieval and classification is proposed. The proposed method effectively describes HRSI and, hence, considerably reduces the semantic gap. The local representation is achieved by an almost complete description of key points, through the proposed color-texture-structure-spectral-speeded-up robust features (CTSS-SURF) descriptor. The CTSS-SURF can effectively overcome the challenges of HRSI, such as scale, illumination, shift, and rotation variation. The regional representation is achieved by dividing images into several parts and then designing regional feature vectors. For both representations, an improved procedure for dictionary creation is proposed to increase the dictionary discriminative ability and reduce the computational cost. An extensive experimental evaluation on the UC Merced Land Use Dataset has been performed and compared with other feature extraction methods. For retrieval, the proposed method achieves 81.24 in mean average precision value and an accuracy of 98.23% for scene classification. It outperforms many state-of-the-art methods, including convolutional neural networks. The impressive results demonstrate the superiority of the proposed approach for HRSI.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$25.00 © 2019 SPIE
Samia Bouteldja, Assia Kourgli, and Aichouche Belhadj Aissa "Efficient local-region approach for high-resolution remote-sensing image retrieval and classification," Journal of Applied Remote Sensing 13(1), 016512 (31 January 2019). https://doi.org/10.1117/1.JRS.13.016512
Received: 20 July 2018; Accepted: 3 January 2019; Published: 31 January 2019
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