End-to-end impact crater detection and identification method based on fully convolutional neural network structure

Abstract

The invention discloses an end-to-end impact crater detection and identification method based on a fully convolutional neural network structure. According to the method, detection and identification of impact craters in a celestial remote sensing image are realized synchronously. A network established according to the method is named as CraterIDNet. The established network is composed of two impact crater detection channels and an impact crater identification channel. Network weight parameters are only composed of convolutional layers and have no fully connected layers. According to the method, for the impact crater detection channels, a candidate box scale optimization and density adjustment mechanism is provided, the optimum candidate box selection is realized, the detection performancefor small impact crater targets is greatly improved, moreover, synchronous detection is carried out on multiscale impact crater targets through utilization of different receptive fields, and the network has the capability of detecting the large-scale range changing impact crater targets. For the impact crater identification channel, grid pattern graphs with rotation and scale invariance are generated through utilization of a grid pattern layer, so the impact crater identification is realized without establishing a matching feature database According to the method, the identification robustnessis improved.

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 ...

AI Technical Summary

Problems solved by technology

This algorithm needs to store the feature database, takes up a lot of storage space, and takes a long time to search and match

When a false target appears in the field of view, it will seriously affect the recognition performance, and this algorithm is extremely sensitive to the position detection error and apparent diameter detection error of the impact crater target, and the robustness is insufficient.

At present, there is no method that can realize the detection and identification of impact craters with large-scale variation range at the same time, and has high accuracy and strong robustness.

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