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SAM weighted KEST hyperspectral anomaly detection algorithm

An anomaly detection and hyperspectral technology, applied in the field of hyperspectral image processing, can solve the problems of not using correlation well, and achieve the effect of suppressing abnormal data

Active Publication Date: 2012-09-12
NANJING UNIV OF SCI & TECH
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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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  • SAM weighted KEST hyperspectral anomaly detection algorithm
  • SAM weighted KEST hyperspectral anomaly detection algorithm
  • SAM weighted KEST hyperspectral anomaly detection algorithm

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[0039] The SAM weighted KEST hyperspectral anomaly detection algorithm of the present invention, firstly, derives the SAM weighted KEST algorithm; secondly, uses a double rectangular window to calculate its SKEST value for each pixel in the hyperspectral image, and performs threshold segmentation to detect abnormal points.

[0040] 1. SAM weighted KEST algorithm:

[0041] It can be seen from the formula [2] that the target and background correlation matrix , The weight of each pixel in is equal, but if the background data is ill-conditioned distribution, the number of abnormal points in the background data or the number of background points in the target data is large, , The distribution of target and background data cannot be fully described, and the target detection efficiency is low. Therefore this patent proposes a kind of SAM weighted KEST algorithm, this method uses , The spectral angle matching (SAM) of each pixel spectral vector and the data center vector ...

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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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Application Information

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IPC IPC(8): G06T7/00
Inventor 柏连发张毅陈钱顾国华韩静岳江王博徐杭威祁伟金左轮
Owner NANJING UNIV OF SCI & TECH
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