Research On The Classification Method Of Remote Sensing Images Based On Texture And Spectral Information Fusion

A new remote sensing object classification algorithm based on the fusion of texture feature and spectral feature is proposed.In the classification algorithm which is prompted in this paper, a new kind of target feature named feature spectrum vector is put forward here. Meanwhile, a new kind of feature fusion method that based on vector relativity is prompted here. And then, a new membership function with better performance and the decreasing dimension function is promoted here. Using this function to make projection transformation to the real feature, then the fuzzy feature value can be received. And then, choose a kind of classify rule, and label the targets. At last, the classification results are evaluated by two different methods.Remote sensing images are experimented using the promoted classification algorithm proposed in this paper. First, simulation images of different levels of complexity are experimented and compared with different classification methods based on different features. Second, original image and fusion image are experimented and compare the classification results. Last, use the promoted algorithm to assort experiment of remote sensing images with nine different fusion methods and compare the classification results.Through the experimental analysis, use the promoted algorithm, ie feature fusion with the weighting coefficients, to assort experiment of real remote sensing images. For three kinds of iamge, the overall classification accuracy is 92.60%. But using the unimproved algorithm, ie feature fusion without the weighting coefficients, the overall classification accuracy is 90.15%. For five kinds of iamge, use the promoted algorithm, ie feature fusion with the weighting coefficients, to assort experiment of real remote sensing images, the overall classification accuracy is 91.11%. But using the unimproved algorithm, ie feature fusion without the weighting coefficients, the overall classification accuracy is 87.58%.The images on the use of 9 different fusion methods are classified that the classification results significantly better than before the fusion images. For three kinds of no fusion iamge, the overall classification accuracy is 90.77%, but for the fusion image, the overall classification accuracy is 92.62%; For five kinds of no fusion iamge, the overall classification accuracy is 88.99%, but for the fusion image, the overall classification accuracy is 90.82%.The result suggests that the promoted algorithm can achieve better classification performance. Meanwhile, the classification results of the fusion images significantly better than before the fusion images.