Thermal Infrared Hyperspectral Anomaly Detection Method in Emissivity Domain Based on Blocking and Low-rank Prior

Abstract

The invention relates to a method for detecting abnormality of thermal infrared hyperspectral in emissivity domain based on block and low-rank prior. First, temperature and emissivity inversion were performed on the original image to obtain the emissivity map and temperature map of the image, and then the temperature map and radiance map were combined with the Potts-based image segmentation algorithm to segment the emissivity map into multiple homogeneous regions. It is observed that in locally homogeneous regions, the background signal has an enhanced low-rank nature, while the anomalies manifest spatial sparsity. Based on this observation, background pixels can be low-rank reconstructed from a set of fundamental background signals, while anomalies can be represented by sparse residuals. Then use the extracted background endmembers to perform low-rank sparse matrix decomposition on the original hyperspectral data matrix to remove part of the noise and obtain purer background information than the original image. Then, the Mahalanobis distance is combined with the original emissivity image and the background information to calculate the spectral difference between the anomaly and the background, so as to realize the separation of the anomaly and the background.

Description

technical field [0001] The invention is based on the field of remote sensing image technology processing, and in particular relates to an emissivity domain thermal infrared hyperspectral anomaly detection method based on block and low rank prior. Background technique [0002] Hyperspectral imaging allows simultaneous observation of geometric and physical properties of materials, which makes it possible to precisely distinguish different objects. Target detection is one of the important research fields of hyperspectral information processing. Hyperspectral target detection can generally be divided into anomaly detection and feature-based target detection. In anomaly detection tasks, there is no prior knowledge about anomalies or backgrounds, where anomalies are observations that differ significantly in spectral signature from adjacent backgrounds. The radiation in the long-wave infrared spectral region measured by the sensor mainly comes from the emission of the surface obj...

AI Technical Summary

Problems solved by technology

Second, traditional statistical methods are difficult to accurately describe the complex background of hyperspectral images

Third, because the thermal infrared sensor is very sensitive, the signal-to-noise ratio of the thermal infrared image is relatively low, and the noise can easily cover up the spectral characteristics of the ground object. In addition, the emissivity spectrum of the object has a small change range, so the distance between the target and the background is relatively low. The spectral contrast of the

Due to the existence of the above-mentioned problems, the anomaly detection performance of thermal infrared hyperspectral images is often poor.

Images