A hyperspectral remote sensing image target detection method

Abstract

The invention discloses a hyperspectral remote sensing image target detection method. The multi-task learning method is introduced into the hyperspectral remote sensing image target detection. The multi-task learning method fully considers the information redundancy between the hyperspectral remote sensing image bands, and proposes a joint sparse expression and target detection algorithms for multi-task learning. The multi-task learning method can use the redundancy between the hyperspectral remote sensing image bands to extract multiple sub-data sets, build multi-detection tasks, and use the data correlation of different tasks to better learn the pixel spectrum based on the sparse expression model, thereby improving detection effect. At the same time, the multi-task learning method can reduce the redundant interference effect while retaining enough discriminative information.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing image processing, and relates to a hyperspectral remote sensing image target detection method, in particular to a hyperspectral remote sensing image target detection method combining sparse expression and multi-task learning. Background technique [0002] Hyperspectral remote sensing imagery combines traditional two-dimensional imaging remote sensing technology and spectral technology, and has the characteristics of high spectral resolution and map-spectrum integration. Each pixel on the image has spectral information of dozens or even hundreds of bands, which can provide diagnostic spectral feature information for distinguishing different substances. Therefore, hyperspectral remote sensing images have the ability to distinguish subtle spectral differences between different substances Ability. This characteristic of hyperspectral remote sensing images enables it to effectivel...

AI Technical Summary

Problems solved by technology

The classic feature transformation method is the principal component analysis method. However, since the target pixels only occupy a small part of the image, the components containing the target energy are often discarded during the principal component transformation process, resulting in lower detection results.

Therefore, the existing target detection methods are still unable to preserve enough discriminative information to distinguish the target from the background while avoiding redundant interference effects.

Images