Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Abstract

Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

HA-RoadFormer: Hybrid Attention Transformer with Multi-Branch for Large-Scale High-Resolution Dense Road Segmentation

“…According to the structure of HA-RoadFormer, the remote sensing image needs to be down-sampling 32 times, and 512 is exactly a multiple of 32. Considering the memory of hardware devices and segmentation results, the remote sensing images and the corresponding label images in the dataset are randomly cut into 512 × 512 [43] pixel image samples. Then, all the image samples are randomly divided into a new training set and validation set in the proportion of 4:1 because 20 percent of the verification set is widely used [44].…”

Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

“…Machine learning technology has developed rapidly in recent years, especially with the proposal of the Fully Convolutional Network (FCN) [13], which is a milestone in the field of image processing research and has achieved good results for efficient image segmentation. There have been many deep-learning-based studies related to remote sensing segmentation recently [2,[14][15][16][17][18][19][20][21][22][23][24][25][26]. For the most relevant problems in road extraction research , we briefly review related works, including refined road boundary extraction and continuous road regional recognition.…”

Semantic Segmentation of Remote Sensing Images Depicting Environmental Hazards in High-Speed Rail Network Based on Large-Model Pre-Classification

“…Due to its efficiency, high image resolution, wide coverage, and simple method of data acquisition, remote sensing technology has been used to monitor hazards in the environment of railway networks [ 6 ]. Currently used target detection technology based on remote sensing images primarily focuses on various targets, such as buildings [ 7 , 8 ], water resources [ 9 , 10 ], and farmland [ 11 , 12 ]. The study of railway hazards has emerged in recent years, and existing work has only been directed at the target of color-coated steel sheet roof buildings.…”