Remote sensing image semantic segmentation method and device, computer equipment and storage medium

Abstract

The invention discloses a remote sensing image semantic segmentation method and device, computer equipment and a storage medium, and the method comprises the steps of obtaining a preprocessed remote sensing image, carrying out the high-frequency texture feature and low-frequency semantic feature extraction of the preprocessed remote sensing image based on a feature extraction network layer, and taking the extracted features as an input feature set; introducing the low-frequency semantic features into a spatial pyramid pooling module for multi-scale pooling, and obtaining aggregated text features; introducing the input feature set and the aggregated text features into a feature guide alignment module, and obtaining an aligned input feature set according to the difference between the input feature set and the aggregated text features; introducing the aligned input feature set and the aggregated text features into a gating feature selection module for selective fusion to obtain an aligned and fused supplementary feature set; and according to the supplementary feature set and the aggregated text features, performing splicing fusion to generate features, processing the features based on a preset performance function, and performing prediction classification on the processed features to obtain a final feature layer. The segmentation precision is effectively improved.

Description

technical field [0001] The invention relates to the technical field of aerospace remote sensing, in particular to a remote sensing image semantic segmentation method, device, computer equipment and storage medium. Background technique [0002] Semantic segmentation refers to classifying each pixel in an image and marking pixels belonging to the same category as the same token. Semantic segmentation, as a core research field in computer vision, is the basis of image interpretation. With the rapid development of deep learning in recent years, semantic segmentation also has a huge application background in high-resolution remote sensing images, such as: land use mapping, urban planning, agricultural insurance, etc. [0003] The current research algorithms in the field of remote sensing image segmentation can be divided into two categories: traditional methods based on manual feature description, and deep learning methods based on convolutional neural networks (CNN). Tradition...

AI Technical Summary

Problems solved by technology

Although these methods can effectively improve the segmentation results, there are still some limitations in the semantic segmentation of remote sensing images: 1) The high-frequency texture information contained in the deep semantic features is less, and more is lost after passing through the global context aggregation module. High-frequency information, so the segmentation effect on small objects and edges is poor

2) The global context aggregation module has limited discriminative ability in large-scale remote sensing images, so there will be spatial fragmentation predictions in the final segmentation results due to lack of context information

However, due to the influence of a series of convolution and pooling between different feature layers in the feature extraction layer, there will be a phenomenon that the features are difficult to align, which will affect the accuracy of feature fusion.

Images