Research On Hyperspectral Imagery Classification Method By Multiple Kernel Learning Using Multi-resolution Analysis

Hyperspectral imaging achieves detail observation in the spectral dimension,and the spectral resolution has been greatly improved, which provides richdiscriminative information for classification. However, in terms of land-coversclassification, the great high spatial and spectral resolution does not mean theeffective classification. The observation scale of classifier affects the similaritymeasurement of the inter class or the within class, but the observation scale of thetraditional classifier cannot adapt to the data changes automatically. Therefore, thispaper focus on the property of multi-resolution on hyperspectral image, andintegrates spatial information and spectral information by multiple kernellearning(MKL), which can improve the ability of land-covers classification onhyperspectral image.The main research of this paper is to introduce the theory of multi-resolutionanalysis to MKL, which is used to achieve effective classification, three aspects asfollowing in details:First, starting from imaging principle of hyperspectral image and datacharacteristic, the advantages and shortages of MKL in classification are analyzed inthis paper. The measurement property of kernel function is introduced significantlybased on the framework of MKL, providing an opportunity to construct multiplekernels, which visualizes the abstractive mapping feature space. Then, to introducethe linear and non-linear combination of kernels, several typical MKL algorithmsare given in details, providing the theory foundation to expand MKL tomulti-resolution MKL.Second, in order to expand the mapping relationship of kernel and visualize themapping feature, two typical two-dimensional hyperspectral image featureextraction methods are shown. In connection with the correlation between spatialand spectral on hyperspectral image data, the2D feature extraction method isextended to3D feature extraction. More importantly, multi-resolution analysis infeature space is realized by3D-Gabor filter, which can obtain multi-resolutionfeature through scaling feature space. Corresponding experiments are given to provethe superior performance of multi-resolution feature in land-covers classification.At last, taking the multi-resolution analysis theory as the guide, the MKLmodel with the ability of multi-resolution learning is created in kernel space toconstruct MKL classifier based on multi-resolution. Firstly, construct basic kernelbased on feature mapping and scale the mapping space to get the multi-resolution multiple kernels. Then, considering the characteristic of multi-resolution, give thenon-linear combination of multiple kernels and the solving process of MKL tocomplete the classifier design. Finally, validate the multi-resolution MKL onhyperspectral classification to emphasis the advantages of multi-resolution theory onhyperspectral image processing.