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The topographic effect on land surface reflectance is an important factor affecting quantitative analysis of remotely sensed data in mountainous regions. Different approaches have been developed to reduce topographical effects. Of the many methods, the Minnaert correction method is most frequently used for topographic correction, but a single global Minnaert value used in previous research cannot effectively reduce topographic effects on the remotely sensed data, especially in the areas with steep slopes. This paper explores the method to develop a pixel-based Minnaert coefficient image based on the established relationship between Minnaert coefficients and topographic slopes. A texture measure based on homogeneity is used to evaluate the topographic correction result. This study has demonstrated promising in reducing topographic effects on the Landsat ETMϩ image with the pixel-based Minnnaert correction method.

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In this article, we propose an image classification algorithm based on Bag of Visual Words model and multikernel learning. First of all, we extract the D-SIFT (Dense Scale-invariant Feature Transform) features from images in the training set. And then construct visual vocabulary via K-means clustering. The local features of original images are mapped to vectors of fixed length through visual vocabulary and spatial pyramid model. At last, the final classification results are given by generalized multiple kernel proposed by this paper. The experiments are performed on Caltech-101 image dataset and the results show the accuracy and effectiveness of the algorithm.

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