Hyperspectral image target detection method and system based on random forest measure learning

Abstract

The invention provides a hyperspectral image target detection method and system based on random forest measure learning. The method comprises the steps that firstly, n training samples are selected from a hyperspectral remote sensing image X to be detected; t then a mapping function is formed, the formed mapping function serves as input of a decision tree of the random forest to train the random forest, then the trained random forest is formed, average operation is conducted on all the decision trees, and a final measurement distance function is found; a measurement distance detection statistical value between all pixels on the X and a certain target pixel in the training sample are calculated by adopting the measurement distance; and finally, target detection is achieved according to thedetection statistical value. The method has the beneficial effects that the technical scheme provided by the invention has relatively good robustness, the high-dimensional data problem can be well solved, the target detection precision is improved, and the separation of the target pixel and the background pixel is better realized; and higher target detection precision is achieved.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, in particular to a hyperspectral image target detection method and system based on random forest measure learning. Background technique [0002] Spectral remote sensing image processing plays an important role in the detection of material information, and is an important topic in the field of remote sensing. Hyperspectral remote sensing images can provide continuous radiation spectrum bands, carry rich ground information, and can be used to deal with different application fields, such as house change detection, crop evaluation, geological and mineral resource investigation, etc. In the field of hyperspectral remote sensing image processing, target detection is one of the main tasks. The so-called target detection is the process of separating interested pixels from non-target pixels, which is essentially a binary classification problem. The existing target detection method is mainly...

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In addition, most existing metric learning methods are mostly based on a single metric, which cannot handle multi-feature representations well

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