Abstract
Abstract. As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR) and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.
Highlights
With the development of remote sensing technology, there are more and more multi-source remote sensing data of the same area could be obtained, especially the multi-spectral images and Synthetic Aperture Radar (SAR) images
The optical image can reflect spectral information of surface features, and the SAR image can reflect back scattering intensity information of the different surface features and combined surface features To adequately take advantage of the spectral images and the SAR images, multisensor data fusion technique has been proposed in remote sensing image processing and Information Classification on Remote Sensing Image, which is the gist of this paper
All the standard image fusion methods such as IHS, Principal component analysis (PCA), Brovery transformation often result in serious spectral distortion
Summary
With the development of remote sensing technology, there are more and more multi-source remote sensing data of the same area could be obtained, especially the multi-spectral images and SAR images. All the standard image fusion methods such as IHS, PCA, Brovery transformation often result in serious spectral distortion. The emergence of the hybrid image fusion such as Wavelet Transformation solved the problem of the spectral distortion in the image fusion. The Wavelet Transformation has been widely used in various image fusion because of its better fusion effect. Based on the achievements summed up by our predecessors, this paper fuses the domestic airborne SAR images and SPOT5 images by all kinds of fusion methods. Contrasts the fusion precision and selects the best fusion data to be classified based on SVM
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