An end-to-end impact crater detection and recognition method based on a fully convolutional neural network structure

A convolutional neural network structure technology, applied in the field of end-to-end impact crater detection and recognition based on a fully convolutional neural network structure, can solve the impact crater target position detection error and apparent diameter detection error that are extremely sensitive and take up storage space Large, time-consuming search and matching, etc., to achieve the effect of reaching the leading performance level, occupying a small space, and enhancing the robustness of recognition
CN108734219BActive Publication Date: 2022-02-01BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
BEIHANG UNIV
Publication Date
2022-02-01

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Abstract

The invention discloses an end-to-end impact crater detection and identification method based on a fully convolutional neural network structure. The method simultaneously realizes the detection and identification of impact craters in remote sensing images of celestial bodies. The network established by the method is named CraterIDNet, and the established The network consists of two impact crater detection channels and one impact crater identification channel. The network weight parameters are only composed of convolutional layers without full connection layers. The invention proposes a candidate frame scale optimization and density adjustment mechanism for the impact crater detection channel, realizes the optimal candidate frame selection, greatly improves the detection performance of small impact crater targets, and uses different receptive fields to synchronize multi-scale impact crater targets Detection, so that the network has the ability to detect large-scale changes in impact crater targets. For the impact crater identification channel, a grid pattern layer is proposed to generate a rotation and scale invariant grid pattern map to realize impact crater identification without building a matching feature database. The invention enhances the recognition robustness.
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Description

technical field

[0001] The invention relates to the technical field of remote sensing image processing and astronomical autonomous navigation, in particular to an end-to-end impact crater detection and identification method based on a fully convolutional neural network structure. Background technique

[0002] Impact craters are the most abundant topographic structures on the surface of celestial bodies, and their morphological characteristics and spatial distribution are important basis for the study of planetary geology. In addition, impact craters are ideal landmarks for autonomous navigation of spacecraft. The detection and identification technology of impact craters is extremely important to the study of planetary geology and the realization of autonomous navigation of spacecraft. Currently, impact crater detection and recognition are studied separately as two independent algorithms. The goal of impact crater detection is to determine whether the image contains impact ...

Claims

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