Remote sensing image classification based on block feature point density analysis and multiple-feature fusion

Shijin Li; Yaping Jiang; Yang Zhang; Jun Feng

doi:10.1117/12.2199216

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8 October 2015 Remote sensing image classification based on block feature point density analysis and multiple-feature fusion

Shijin Li, Yaping Jiang, Yang Zhang, Jun Feng

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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967513 (2015) https://doi.org/10.1117/12.2199216
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

With the development of remote sensing (RS) and the related technologies, the resolution of RS images is enhancing. Compared with moderate or low resolution images, high-resolution ones can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of features. The effectiveness of these features is not the same, but some of them are complementary. Considering the above information and characteristics, a new method is proposed to classify RS images based on hierarchical fusion of multi-features. Firstly, RS images are pre-classified into two categories in terms of whether feature points are uniformly or non-uniformly distributed. Then, the color histogram and Gabor texture feature are extracted from the uniformly-distributed categories, and the linear spatial pyramid matching using sparse coding (ScSPM) feature is obtained from the non-uniformly-distributed categories. Finally, the classification is performed by two support vector machine classifiers. The experimental results on a large RS image database with 2100 images show that the overall classification accuracy is boosted by 10.1% in comparison with the highest accuracy of single feature classification method. Compared with other multiple-feature fusion methods, the proposed method has achieved the highest classification accuracy on this dataset which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.

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Shijin Li, Yaping Jiang, Yang Zhang, and Jun Feng "Remote sensing image classification based on block feature point density analysis and multiple-feature fusion", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967513 (8 October 2015); https://doi.org/10.1117/12.2199216

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