Hyperspectral image classification method based on extended morphology and active learning

Abstract

The invention discloses a hyperspectral image classification method based on extended morphology and active learning, which solves the problem that the prior art can not fully excavate the hyperspectral image space information and leads to low classification accuracy. The steps are as follows: 1) inputting hyperspectral image data; 2) reducing the dimension of the data, extracting spectral features, and obtaining spatial features through morphological section transformation; 3) fusing the space spectrum features to divide the train and test sample sets; 4) classifying that SVM by using the train sample set; 5) performing an active learning cycle, wherein sample marks are selected by that MCLU criterion and the AP clustering, and training and testing sample set are updated; 6) performing SVM classification by using a new training sample set until the number of training samples reaches a preset number, and obtaining the final classification result. The invention combines the morphological characteristics of multiple structural elements with active learning, makes full use of space spectrum information, and improves classification accuracy on the premise of small samples.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to the technical field of hyperspectral image classification, specifically a hyperspectral classification method based on extended morphology and active learning. It is used for object classification in resource exploration, forest cover and disaster monitoring. Background technique [0002] A hyperspectral sensor, that is, a spectrometer, can simultaneously image a specific area in dozens or hundreds of continuous bands, and the obtained image is a hyperspectral image. Since hyperspectral imaging involves different bands, hyperspectral images can obtain rich spectral information, which creates good conditions for object recognition and target detection. In recent years, hyperspectral images have been widely used in the fine identification of minerals, the identification and classification of vegetation types, the distinction of urban features, and the detection of d...

AI Technical Summary

Problems solved by technology

The disadvantage of this classification method is that the extended morphological contour of the image is extracted using structural elements of a single scale, which also has the problem of not being able to fully exploit the spatial information of the hyperspectral image, so satisfactory classification accuracy cannot be obtained; The criterion selects the samples that need to be marked according to the amount of information. The calculation process is complex, time-consuming, and requires a large number of marked samples.

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