Road Extraction from GF-1 Remote Sensing Images Based on Dilated Convolution Residual Network with Multi-Scale Feature Fusion

2021 ◽  
Vol 58 (2) ◽  
pp. 0228001
Author(s):  
马天浩 Ma Tianhao ◽  
谭海 Tan Hai ◽  
李天琪 Li Tianqi ◽  
吴雅男 Wu Yanan ◽  
刘祺 Liu Qi
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution

AF-Net: All-scale Feature Fusion Network for Road Extraction from Remote Sensing Images

2021 ◽  
Author(s):  
Shide Zou ◽  
Fengchao Xiong ◽  
Haonan Luo ◽  
Jianfeng Lu ◽  
Yuntao Qian
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Scale Feature

scholarly journals End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

2021 ◽  
Vol 13 (4) ◽  
pp. 666
Author(s):  
Hai Huan ◽  
Pengcheng Li ◽  
Nan Zou ◽  
Chao Wang ◽  
Yaqin Xie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Model Performance ◽  
Super Resolution ◽  
Structural Similarity ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Multi Scale ◽  
Information Image

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

scholarly journals C-UNet: Complement UNet for Remote Sensing Road Extraction

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2153
Author(s):  
Yuewu Hou ◽  
Zhaoying Liu ◽  
Ting Zhang ◽  
Yujian Li
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Segmentation Result ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Daily Work ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Mode Of Transportation ◽  
Work And Life

Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

scholarly journals Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Shengfu Li ◽  
Cheng Liao ◽  
Yulin Ding ◽  
Han Hu ◽  
Yang Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Long Distance ◽  
Features Fusion ◽  
Multi Scale ◽  
Boundary Recognition ◽  
Benchmark Datasets

Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

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