Degradation robust multi-scale context sensing network for remote sensing image segmentation

By using a degenerate robust multi-scale context-aware network, multi-scale sliding windows and four-way directional convolution kernels are employed to process remote sensing images, solving the problem of difficulty in capturing local context in traditional methods and achieving high consistency and fine-grained effect in remote sensing image segmentation.

CN121482391APending Publication Date: 2026-02-06NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202511584291.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-31
Publication Date
2026-02-06

AI Technical Summary

Technical Problem

Traditional convolutional kernels struggle to capture non-axis-aligned and non-axis-symmetric local contexts in remote sensing images. This leads to the loss of details or semantic confusion during feature extraction when small-scale targets and large-scale structures are intertwined. Existing methods have failed to effectively address the problems of feature extraction and semantic understanding in complex scenes.

Method used

A degenerate robust multi-scale context-aware network is adopted, including a backbone feature extraction network module, a multi-granularity context aggregation module, and a robust four-directional feature fusion module. Through multi-scale sliding window parallel processing and four-way directional convolution kernel decoupling, it breaks through the traditional serial fusion paradigm and enhances the segmentation consistency of complex scenes and the discriminability of objects with special structures and directional characteristics.

Benefits of technology

It significantly improves the segmentation consistency of complex scenes in remote sensing image segmentation, solves the problems of difficult fine edge segmentation and directional small target recognition, reduces the model learning difficulty through structured priors, and enhances the discriminability of objects with special structures and directional characteristics.

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Abstract

The invention relates to the technical field of deep learning and image processing, in particular to a degradation robust multi-scale context sensing network for remote sensing image segmentation, which comprises the following steps: a backbone feature extraction network module receives a remote sensing image and performs multi-stage feature extraction operation on the remote sensing image to obtain an initial feature map; a multi-granularity context aggregation module performs multi-scale context processing on the initial feature map through a multi-scale sliding window to obtain multi-granularity fusion features; and the robust four-directional feature fusion module performs directional component decoupling on the multi-granularity fusion features through the four-way directional convolution kernels, and fuses the directional features output by the four-way directional convolution kernels to obtain a target feature map. According to the scheme, a serial fusion normal form of a traditional multi-scale method can be broken through, up-sampling noise is avoided, and the segmentation consistency of a complex scene is remarkably improved; and the learning difficulty of the model is reduced through structured prior, and the discrimination of objects with special structures and direction characteristics is enhanced.
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