SAM weighted KEST hyperspectral anomaly detection algorithm

Abstract

The invention discloses an SAM weighted KEST hyperspectral anomaly detection algorithm (SKEST). The method includes the steps: firstly, deducing the SKEST algorithm; and secondly, calculating the SKEST value of each image element in a hyperspectral image by the aid of a double-rectangular window, performing threshold segmentation and detecting abnormal points. In the SKEST algorithm, based on the KEST (kernel Eigen space separation transformation) algorithm, a weight factor is introduced into each sample in a DCOR (difference correlation) matrix of a high-dimensional Eigen space detection point neighborhood by means of SAM (spectral angle mapper) measurement, and the weight factor of each sample depends on an included angle between the spectral vector of the sample and a data center of the detection window. Therefore, abnormal data in the detection window are suppressed, the contribution of main compositional data is highlighted, and the DCOR matrix can more effectively describe target and background data distribution difference. Besides, the SAM is robust to spectral energy, and by the aid of a radial basis function, the SKEST algorithm considers both spectral energy difference and spectral curve shape difference of signals, and accordingly conforms to hyperspectral data characteristics more effectively.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing, and in particular relates to the research on a new abnormality detection algorithm in hyperspectral image target detection. Background technique [0002] In hyperspectral image target detection, due to practical application requirements, many anomaly detection algorithms that do not require prior spectral knowledge of the target have been proposed at home and abroad. These algorithms make full use of the difference in spectral characteristics between outliers and their neighbors, and calculate the statistical characteristics of local areas to achieve target detection. [0003] Currently commonly used anomaly detection algorithms mainly include RX anomaly detection method and subspace projection method (including Principal Component Analysis (PCA), Eigenspace Separation Transform (EST), etc.). The RX algorithm calculates the Mahalanobis distance between the spectral vector of the det...

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Problems solved by technology

[0012] Although the EST algorithm can distinguish between the target and the background to a certain extent, it does not make good use of the correlation between the spectral bands. Therefore, the KEST algorithm is used to map the linear spatial spectral signal into a high-dimensional feature space for outlier detection. Mining the nonlinear information between each spectral band, extracting the high-dimensional features of the target and the background, so as to better distinguish the target and the background

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