High resolution remote sensing image segmentation based on dual-modal efficient feature learning
With the rapid development of spatial technology,the resolution of remote sensing images gradually improves.The detailed information and spatial information contained in remote-sensing images are also richer.The ensuing problems are that the difference between various categories becomes and the difference between the same categories becomes larger,i.e.,the phenomenon of the same spectrum of foreign objects and the different spectrum of the same objects is serious.However,the existing dual-modal segmentation methods do not extract the dual-modal feature information of remote-sensing images separately,and the fusion features are insufficient.The details of upsampling recovery are also insufficient,resulting in the inability to accurately and efficiently learn remote-sensing image information,thereby resulting in segmentation errors,edge blur,and other problems.This study proposes a high resolution remote-sensing image segmentation based on dual-modal efficient feature learning.The algorithm designs appropriate encoders for different modal remote sensing images,efficiently extracts dual-modal features,and reduces the differences between different path features through interactive reinforcement modules.Then,the dual-modal feature aggregation module and the deep feature-extraction module are proposed to further fuse and extract the dual-modal features.As a result,the network can fully learn the complementary information of the dual-modal.Finally,a multi-layer feature upsampling module is proposed,which uses high-level features with rich semantic information to weight the low-level features with rich detail information.Gradual upsampling is then conducted to achieve efficient feature recovery and improve segmentation performance.In this paper,experiments on the Potsdam and Vaihingen datasets demonstrate that the overall accuracy reaches 94.52％and 90.45％,respectively.Experimental results show that the segmentation effect of the proposed algorithm is better than that of existing algorithms.The proposed algorithm can efficiently extract and fuse the multi-modal complementary features of high resolution remote-sensing images and improve the segmentation accuracy of remote-sensing images.This study proposes a high-resolution remote-sensing image segmentation based on dual-modal eficient feature learning.Experiments on the ISPRS Potsdam and Vaihingen datasets show that the proposed model is more suitable for segmenting low vegetation and trees,buildings,and roads with very similar spectral features.It can also achieve the accurate segmentation of small targets,such as cars.However,the complexity of the model needs to be further reduced,and much room for improvement in accuracy remains.In the future,a better segmentation network will be designed to fuse more than two modal features and thus obtain more feature information to achieve more accurate remote sensing image segmentation.
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