Hyperspectral abnormity detection method based on digital image morphology theory

An anomaly detection, digital image technology, applied in the field of hyperspectral anomaly detection, can solve the problem of ignoring the spatial correlation of pixels, achieve good detection effect, reduce false alarm probability, and smooth the image effect

Inactive Publication Date: 2012-06-06
HARBIN ENG UNIV
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Problems solved by technology

[0004] However, these improved algorithms are currently processed from the perspective of data spectral information and feature space analysis, ignoring the spatial correlation between pixels

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  • Hyperspectral abnormity detection method based on digital image morphology theory
  • Hyperspectral abnormity detection method based on digital image morphology theory
  • Hyperspectral abnormity detection method based on digital image morphology theory

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Embodiment Construction

[0026] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0027] The present invention adopts following technical scheme:

[0028] Firstly, the closed operation of extended morphology is used to extract the band features of the hyperspectral image to achieve the purpose of dimensionality reduction. Specific steps are as follows:

[0029] 1. Draw the cross-correlation curve of the hyperspectral image.

[0030] 2. Taking the minimum value point of the cross-correlation coefficient and the point at which ρ is the threshold T as the boundary point to perform partition processing. To achieve the purpose of band region expansion.

[0031] 3. Calculate the cross-correlation coefficient for the mean values ​​of two adjacent band areas, and if the cross-correlation coefficient is greater than the threshold, the two adjacent band areas are merged. Perform band region erosion.

[0032] 4. Calculate the cross-correlation coeffi...

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Abstract

The invention provides a hyperspectral abnormity detection method based on the digital image morphology theory. The method is characterized by firstly carrying out band characteristic extraction on the hyperspectral images by adopting close operation of extended morphology to reach the aim of dimension reduction, being capable of smoothening spectral data while carrying out band selection according to closed transform to remove redundancy, avoiding discontinuity of band information and effectively combining the space information of the ground objects and the information of correlation betweenfine spectra and space; and then carrying out abnormity detection on the hyperspectral image information undergoing dimension reduction, adopting a KRX operator to carry out abnormity detection on the images, obtaining the grayscale images of the detection results and then filtering the detection results by utilizing area close-open operation (ACO) of grayscale morphology to obtain the final detection result. The method not only can be used in combination with the KRX operator but also can be used in combination with other operators for hyperspectral image abnormity detection. The method has strong transportability and can more easily meet the requirement of hyperspectral detection.

Description

technical field [0001] The invention relates to a hyperspectral anomaly detection method. Background technique [0002] Target detection and recognition using hyperspectral images is one of the research hotspots in the field of remote sensing image processing. Anomaly detection is able to detect objects with spectral differences from the surrounding environment without any prior information. Among them, the typical one is the RX method proposed by Reed and Yu. This algorithm is based on the multivariate normal distribution and finds outliers by calculating the Mahalanobis distance between the checkpoint data and the background data. In reality, the distribution of ground features is complex and changeable, which makes it difficult to satisfy the multivariate normal distribution of hyperspectral data; at the same time, because only the low-order statistical characteristics of hyperspectral data are used, the hundreds of bands of hyperspectral data are ignored. The rich nonl...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T5/00
Inventor 赵春晖尤佳万建王玉磊
Owner HARBIN ENG UNIV
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