Hyperspectral target detection method based on multi-example deep convolutional memory network

Abstract

The invention discloses a hyperspectral target detection method based on a multi-example deep convolutional memory network, and mainly solves the problem of poor detection effect of an inaccurately marked hyperspectral target in a complex scene in the prior art. The method comprises the following steps: 1, dividing an input image to obtain a training set and a test set; 2, building a multi-exampledeep convolutional memory network N which sequentially comprises 11 layers of structures including an input layer, three repeated structural units in the middle and an output layer; 3, setting training parameters, performing iterative training on the multi-instance deep convolutional memory network N by using the training set, and stopping training when the network performance is not improved anymore or reaches the maximum training frequency to obtain a finally trained network N'; and 4, inputting the test sample set into the finally trained network N'for detection to obtain a detection result. According to the method, the detection result of the hyperspectral target which is inaccurately marked is improved, the over-fitting phenomenon is reduced, and the method can be used for explosivedetection and crop fine classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral target detection method, which can be used for explosive detection and fine classification of crops. Background technique [0002] Due to the rich spatiotemporal information of hyperspectral images, it has been widely used in various fields such as explosive detection and fine classification of crops in recent years. However, due to the accuracy of the sensor, a pixel marked as an object in the hyperspectral image does not necessarily exist in the ground truth, but indicates that the object exists in a certain range of space including the pixel. In addition, because the background is complex and diverse, and the number of targets is much smaller than that of the background in most cases, it becomes difficult to detect targets in hyperspectral images. [0003] Multi-instance learning originated from drug activity detection. With its increasingly wid...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that when the training data is unbalanced, it is difficult for the standard SVM classifier to obtain a good classification effect, and when the amount of data is large, the solution process is long

The disadvantage of this method is that when the amount of data is large, the memory space required for the feature map is huge, and it takes a long time to train the SVM classifier.

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