scholarly journals AIRPORT RUNWAY SEMANTIC SEGMENTATION BASED ON DCNN IN HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 361-366
Author(s):  
Z. C. Men ◽  
J. Jiang ◽  
X. Guo ◽  
L. J. Chen ◽  
D. S. Liu
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Semantic Segmentation ◽  
Cross Entropy ◽  
Remote Sensing Images ◽  
Data Set ◽  
Entropy Loss ◽  
South Korean ◽  
Airport Detection ◽  
Airport Runway

Abstract. Due to the diverse structure and complex background of airports, fast and accurate airport detection in remote sensing images is challenging. Currently, airport detection method is mostly based on boxes, but pixel-based detection method which identifies airport runway outline has been merely reported. In this paper, a framework using deep convolutional neural network is proposed to accurately identify runway contour from high resolution remote sensing images. Firstly, we make a large and medium airport runway semantic segmentation data set (excluding the south Korean region) including 1,464 airport runways. Then DeepLabv3 semantic segmentation network with cross-entropy loss is trained using airport runway dataset. After the training using cross-entropy loss, lovasz-softmax loss function is used to train network and improve the intersection-over-union (IoU) score by 5.9%. The IoU score 0.75 is selected as the threshold of whether the runway is detected and we get accuracy and recall are 96.64% and 94.32% respectively. Compared with the state-of-the-art method, our method improves 1.3% and 1.6% of accuracy and recall respectively. We extract the number of airport runway as well as their basic contours of all the Korean large and medium airports from the remote sensing images across South Korea. The results show that our method can effectively detect the runway contour from the remote sensing images of a large range of complex scenes, and can provide a reference for the detection of the airport.

scholarly journals River Segmentation of Remote Sensing Images Based on Composite Attention Network

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Zhiyong Fan ◽  
Jianmin Hou ◽  
Qiang Zang ◽  
Yunjie Chen ◽  
Fei Yan
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Cross Entropy ◽  
Important Research ◽  
Dice Coefficient ◽  
Remote Sensing Images ◽  
Training Process ◽  
Attention Network ◽  
Evaluation Indexes ◽  
Agricultural Planning

River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research.

scholarly journals AttentionBased Deep Feature Fusion for the Scene Classification of HighResolution Remote Sensing Images

2019 ◽  
Vol 11 (17) ◽  
pp. 1996 ◽  
Author(s):  
Zhu ◽  
Yan ◽  
Mo ◽  
Liu
Keyword(s):  
Remote Sensing ◽  
Loss Function ◽  
Feature Fusion ◽  
Cross Entropy ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Graphic Processing Units ◽  
Entropy Loss ◽  
Deep Feature

Scene classification of highresolution remote sensing images (HRRSI) is one of the most important means of landcover classification. Deep learning techniques, especially the convolutional neural network (CNN) have been widely applied to the scene classification of HRRSI due to the advancement of graphic processing units (GPU). However, they tend to extract features from the whole images rather than discriminative regions. The visual attention mechanism can force the CNN to focus on discriminative regions, but it may suffer from the influence of intraclass diversity and repeated texture. Motivated by these problems, we propose an attention-based deep feature fusion (ADFF) framework that constitutes three parts, namely attention maps generated by Gradientweighted Class Activation Mapping (GradCAM), a multiplicative fusion of deep features and the centerbased cross-entropy loss function. First of all, we propose to make attention maps generated by GradCAM as an explicit input in order to force the network to concentrate on discriminative regions. Then, deep features derived from original images and attention maps are proposed to be fused by multiplicative fusion in order to consider both improved abilities to distinguish scenes of repeated texture and the salient regions. Finally, the centerbased cross-entropy loss function that utilizes both the cross-entropy loss and center loss function is proposed to backpropagate fused features so as to reduce the effect of intraclass diversity on feature representations. The proposed ADFF architecture is tested on three benchmark datasets to show its performance in scene classification. The experiments confirm that the proposed method outperforms most competitive scene classification methods with an average overall accuracy of 94% under different training ratios.

scholarly journals Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6742
Author(s):  
Yongshi Jie ◽  
Xianhua Ji ◽  
Anzhi Yue ◽  
Jingbo Chen ◽  
Yupeng Deng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Edge Detection ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Power Station ◽  
Data Set ◽  
Photovoltaic Panels ◽  
Power Stations ◽  
Photovoltaic Power

Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of photovoltaic panels in a refined manner. Comparative experiments conducted on the Duke California Solar Array data set and a self-constructed Shanghai Distributed Photovoltaic Power Station data set show that, compared with SegNet, LinkNet, UNet, and FPN, the proposed method obtained the highest identification accuracy on both data sets, and its F1-scores reached 84.79% and 94.03%, respectively. These results indicate that effectively combining multi-layer features with a gated fusion module and introducing an edge detection network to refine the segmentation improves the accuracy of distributed photovoltaic power station identification.

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 Semantic Segmentation of Remote Sensing Images With Sparse Annotations

2021 ◽  
pp. 1-5
Author(s):  
Yuansheng Hua ◽  
Diego Marcos ◽  
Lichao Mou ◽  
Xiao Xiang Zhu ◽  
Devis Tuia
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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