Polarimetric SAR (synthetic aperture radar) terrain classification method based on semantic information and polarimetric decomposition

Abstract

The invention discloses a polarimetric SAR (synthetic aperture radar) terrain classification method based on semantic information and polarimetric decomposition. The polarimetric SAR terrain classification method includes performing mean shift on a span image, extracting a sketch map of the span image, extracting a line segment gathering region in the sketch map by an region extracting technology based on the semantic information, merging span image mean shift over-segmentation regions based on the line segment gathering region as well as by the aid of a critical region majority vote merging strategy and based on a polarimetric feature merging strategy, acquiring an image segmentation result, and fusing the image segmentation result based on the semantic information and an H / alpha-Wishart classification result based on an MRF (markov random field) to acquire a final classification result. The semantic information, an image processing technique and a polarimetric scattering property are combined by the polarimetric SAR terrain classification method, the problem of bad region consistency of existing classification techniques based on the polarimetric decomposition to classification results with gathering-featured surface features (like forest, building groups and the like) is mainly solved, and region consistency and boundary retainability of the classification results with the gathering-featured surface features are improved.

Description

Technical field [0001] The invention belongs to the technical field of image processing and remote sensing, and relates to the classification of polarized SAR images. Specifically, it is a method for classification of polarized SAR ground objects based on semantic information and polarization decomposition. Resolve the ground object classification of polarized SAR images. Background technique [0002] Polarimetric Synthetic Aperture Radar (POLSAR) image processing is an important subject of national defense construction and economic development, and has attracted more and more attention and research. Compared with the ordinary single-polarization synthetic aperture radar (Synthetic Aperture Radar, SAR), the polarization SAR is a full-polarization measurement, can obtain more abundant ground information of the target, and provide more in-depth research on the scattering characteristics of the target. An important basis. Polarimetric SAR object classification is one of the import...

AI Technical Summary

Problems solved by technology

Therefore, there are many defects in the traditional polarimetric SAR ground object classification method including the above method: (1) the regional consistency of the same ground object is not good, resulting in a classification result map of salt and pepper noise; (2) based on the traditional image processing method The polarimetric SAR ground object classification method, for the ground objects with light and dark gray scale changes, such as the traditional classification method based on pixel point and super pixel combination, it is difficult to classify such ground objects into one category; (3) for Complex ground features, such as building groups, since the ground features themselves contain houses, roads, etc., the scattering characteristics of the ground features are not single, and have light and dark ground features, so it is difficult to divide them into a complete area. Extracting various underlying features and using various methods of region merging are difficult to group these regions together, but for the classification of low-resolution polarimetric SAR images, it should be divided into one from the perspective of human vision and image understanding. kind

[0005] To sum up, the pixel classification methods of the above-mentioned polarization SAR ground object classification methods are fine, but there are still some defects, especially for the ground objects with aggregation characteristics (such as buildings, forests, etc.). Single, with light and dark ground object scattering characteristics, poor consistency of classification area, and the boundary is easily affected by noise, and it is easy to produce salt-and-pepper classification results

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