Hyperspectral remote sensing small target detection method based on multiple aperture information processing

Abstract

The invention discloses a hyperspectral remote sensing small target detection method based on multiple aperture information processing. The hyperspectral remote sensing small target detection method based on the multiple aperture information processing comprises data observation, information processing and target detection. In the data observation, distributed parallel partitioned information processing performed on hyperspectral remote sensing data through a multiple aperture asynchronous mapping mechanism is simulated, partitioned data collection is performed on the hyperspectral remote sensing through a data collection chip, multiple aperture information obtaining through a fly visual system and a mapping mechanism are simulated, and the hyperspectral remote sensing spectrum data is decomposed and recombined according to an ommatidium mapping function to form into a spectrum data cube which is about local ground objects and construct a distributed parallel partitioned information processing mode. In the information processing, a cartridge system of the fly visual system is simulated to achieve integration of redundant compression and super sensitivity. In the object detection, a self-adaption mechanism of optic high order nerve cells of the fly visual system is simulated and an anomaly detection result is obtained through a self-adaption small target detection algorithm in combination with judgment of whole situation anomaly and local anomaly.

Description

technical field [0001] The invention relates to a hyperspectral remote sensing abnormal small target detection method based on multi-aperture information processing, and belongs to the technical fields of remote sensing information processing and bionic vision. Background technique [0002] Hyperspectral remote sensing anomaly detection does not require prior knowledge guidance, atmospheric correction, and radiation calibration processing. It has important theoretical and practical values ​​in the application fields of national defense and military security, environmental pollution monitoring, and mineral energy detection. However, the fine feature attribute description of hyperspectral remote sensing images at large spatial observation scales leads to the diversification of ground feature types, complex spatial distribution, and fuzzy spectral features. new challenge. [0003] Traditional anomaly detection algorithms can be divided into differentiating background and anoma...

AI Technical Summary

Problems solved by technology

At present, there are no reports on the use of the advantages of the fly visual system to solve the problems of abnormal small target detection.

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