Remote sensing image semantic segmentation method based on pyramid pooling multistage feature fusion network

Abstract

The invention discloses a remote sensing image semantic segmentation method based on a pyramid pooling multistage feature fusion network. The method comprises steps of employing ResNet to extract features, respectively introducing the features extracted at each stage of ResNet into a spatial pyramid pooling structure to extract the multi-scale information of a target, and introducing a double attention module to the last part of feature extraction, and a multi-level feature fusion strategy is adopted to perform feature fusion on the pooled features and the features acquired by the double attention module so that refined classification of the remote sensing images can be realized.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, belongs to remote sensing image surface object classification technology, and in particular relates to a remote sensing image semantic segmentation method based on a pyramid pooling multi-level feature fusion network. Background technique [0002] Remote sensing images have the characteristics of large coverage and intuitive reflection of the surface. The classification of remote sensing images is widely used in land monitoring, environmental monitoring, and map making. [0003] The current mainstream image semantic segmentation methods can be roughly divided into two categories, traditional machine learning methods and deep learning-based methods. The traditional machine learning method uses the color, texture, shape and spatial position relationship of the object to extract features, and then uses clustering, classification and other algorithms to segment the image. However, t...

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Problems solved by technology

[0004] The existing image semantic segmentation methods have improved or solved the semantic segmentation problem of complex image scenes to a certain extent. Compared with natural images, the features in remote sensing images are complex and diverse, and the technology of using image semantic segmentation to realize remote sensing image feature classification still needs to be solved. promote

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