scholarly journals MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer

2021 ◽  
Vol 13 (23) ◽  
pp. 4743
Author(s):  
Wei Yuan ◽  
Wenbo Xu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Global Features ◽  
Adaptive Network ◽  
Data Set ◽  
Multi Scale

The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.

scholarly journals A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

2020 ◽  
Vol 9 (4) ◽  
pp. 189 ◽  
Author(s):  
Hongxiang Guo ◽  
Guojin He ◽  
Wei Jiang ◽  
Ranyu Yin ◽  
Lei Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Water Body ◽  
Semantic Segmentation ◽  
Water Bodies ◽  
Water Extraction ◽  
Remote Sensing Images ◽  
Multi Scale

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

scholarly journals High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries

2021 ◽  
Vol 13 (21) ◽  
pp. 4237
Author(s):  
Xiaoping Zhang ◽  
Bo Cheng ◽  
Jinfen Chen ◽  
Chenbin Liang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Morphological Characteristics ◽  
Scientific Management ◽  
Spatial Gradient ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Precise Identification

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

scholarly journals Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259283
Author(s):  
Wentong Wu ◽  
Han Liu ◽  
Lingling Li ◽  
Yilin Long ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Image Feature ◽  
Remote Sensing Images ◽  
Data Set ◽  
Detection Ability ◽  
Small Targets

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

scholarly journals Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

2021 ◽  
Vol 13 (3) ◽  
pp. 504
Author(s):  
Wanting Yang ◽  
Xianfeng Zhang ◽  
Peng Luo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Remote Sensing Data ◽  
Remote Sensing Images ◽  
Learning Network ◽  
Building Information ◽  
Deep Learning Network

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

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