Change detection is an important research direction in the field of remote sensing technology. However, for hyperspectral images, the nonlinear relationship between the two temporal images will increase the difficulty of judging whether the pixel is changed or not. To solve this problem, a hyperspectral change detection method is proposed in which the transformation matrices are obtained by using the constraint formula based on the minimum spectral angle, which uses both spectral and spatial information. Further, a kernel function is used to handle the nonlinear points. There are three main steps in the proposed method: first, the two temporal hyperspectral images are transformed into new dimensional space by a nonlinear function; second, in the dimension of observation, all the observations are combined into a vector, and then the two transformation matrices are obtained by using the formula of spectral angle constraint; and third, each pixel is given weight with a spatial weight map, which combined the spectral information and spatial information. Study results on three data sets indicate that the proposed method performs better than most unsupervised methods.
Affected by the sensor itself, illumination, atmosphere, terrain and other factors, even if imaging the same region at the same time, the spectral characteristics of ground objects in different remote sensing images are also very different, and the surface parameters, ground object classification and target recognition results of the inversion are also different, which brings great uncertainty to quantitative analysis. The relative radiation correction effect of PIF, method is obvious and the operation is simple, and the accuracy of the effect depends greatly on the selection of the PIF point. The general relative radiometric correction methods are linearization correction without considering the nonlinear difference of multi-temporal images. At present, most radiation normalization methods assume that the transformation relation between images is linear, extract PIF points and establish radiation transformation model. In this paper, Kernel Canonical Correlation Analysis (KCCA) is used for the first time to normalize the radiation between multi-temporal hyperspectral images, which can greatly reduce the nonlinear difference in relative radiation correction. Based on the theory of nuclear canonical correlation analysis, the radiation normalization method of multi-temporal aerial hyperspectral images is proposed. The feature points of PIF are extracted in the nuclear projection space, and the nonlinear model is used for the radiation normalization of hyperspectral images, to improve the radiation normalization accuracy of multi-temporal hyperspectral images. Compared with Canonical Correlation Analysis (CCA), the number and precision of PIF point extraction can be significantly improved. This method can satisfy the radiation normalization between aerial hyperspectral multi-temporal images.
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