Polarized SAR image classification method based on long-short-term memory recurrent neural network

Abstract

The invention discloses a polarized SAR image classification method based on a long-short term memory recurrent neural network. The method comprises the following steps: carrying out delicate Lee filtering processing on polarized SAR data; carrying out super-pixel segmentation on the filtered polarization coherence matrix; obtaining a multi-dimensional characteristic polarization SAR image by using the spatial information of the polarization SAR image; obtaining sample data and test data of the polarimetric SAR image to be classified; carrying out deep learning on the long-short term memory network by using the sample data; classifying the test data by using the trained long-short time memory network; and obtaining a classification label, and obtaining a color classification result graph.According to the method, spatial information of the polarized SAR images is combined, the polarized SAR images with multi-dimensional features are obtained, the polarized SAR image data with the multi-dimensional features are used as input of the long-short time memory network, the classification accuracy of the polarized SAR images is effectively improved, and the method can be used for ground feature classification and recognition of the polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarization SAR image classification method based on a long-short-term memory cycle neural network, which can be applied to ground object classification and target recognition of polarization SAR images. Background technique [0002] SAR is a synthetic aperture radar, an advanced remote sensing imaging system, which has the advantages of all-day, all-weather, and high resolution. Polarization SAR is a multi-channel synthetic aperture radar. It is an important part of SAR. Compared with SAR, it can obtain more information about the target. The interpretation of polarimetric SAR images has more research value than other ordinary remote sensing images. , in many fields (such as census of earth resources, flood monitoring, identification of vegetation types, detection of ships on the sea surface, and analysis of surface feature characteristics, etc.), polariza...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a polarimetric SAR image classification method based on long short-term memory recurrent neural network, which is used to solve the problem of vector-based classification methods when representing polarimetric SAR pixels. The technical problem of low image classification accuracy caused by the loss of spatial information

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