Hyperspectral image classification method based on MNF (Minimum Noise Fraction) transform in combination with extended attribute filtering

Abstract

The invention discloses a hyperspectral image classification method based on MNF (Minimum Noise Fraction) transform in combination with extended attribute filtering. The method comprises the steps of 1) performing MNF transform to a hyperspectral remote sensing image; 2) selecting MNF component number and selecting the MNF component number to be reserved according to two constraints, i.e. the characteristic value of band number and the gradient of the characteristic values of adjacent bands; 3) executing EAP (Extended Attribute Profile) operation to each MNF component; 4) stacking the image attribute open profile and the attribute closed profile of each MNF component and the component and performing classification by adopting a K-type SVM (Support Vector Machine) to obtain a final hyperspectral classified image. The method can realize noise reduction through MNF and can effectively reduce the dimensionality of hyperspectral data at the same time. On the basis, spectral information after dimensionality reduction and texture information obtained after EAP filtering are combined, then the characteristics that the K-type SVM can reduce the computation cost and simultaneously has performance similar to an RBF (Radial Basis Function) kernel are utilized and the classification accuracy of the hyperspectral remote sensing image is improved.

Description

technical field [0001] The invention relates to a hyperspectral remote sensing image classification method using MNF transformation combined with extended attribute filtering, and belongs to the technical field of remote sensing image processing. Background technique [0002] Hyperspectral images can organically combine the spectral information reflecting the target with the image information reflecting the spatial and geometric relationship of the target, and are currently widely used in various fields of the national economy. [0003] Currently commonly used hyperspectral image classification algorithms can be divided into supervised and unsupervised algorithms. Traditional supervised classification methods include spectral angle mapping method, parallelepiped method, maximum likelihood method, minimum distance method, and Mahalanobis distance method; traditional unsupervised classification methods include IsoData method, K-Means method, etc. In addition to the above trad...

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Problems solved by technology

However, these methods use a fixed window to obtain spatial information, which leads to the selection problem of scale

Another classification method is to integrate spatial information into a multi-kernel learning method, which also has the problem of size ratio s...

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