scholarly journals Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2335 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Peng Xin ◽  
Shuai Li ◽  
Min Qi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Feature Fusion ◽  
Deep Layer ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Fully Convolutional Neural Networks ◽  
Aerial Remote Sensing ◽  
Detection Speed

To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images.

scholarly journals End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks

2018 ◽  
Vol 10 (10) ◽  
pp. 1516 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Shuai Li ◽  
Hongxiao Feng ◽  
Shiping Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Convolutional Neural Networks ◽  
Detection Rate ◽  
Detection Methods ◽  
Detection Time ◽  
Remote Sensing Images ◽  
Threshold Detection ◽  
Airport Detection

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.

scholarly journals Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Jingxian Yu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Collapsed Buildings ◽  
Improved Model ◽  
Detection Speed

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

scholarly journals Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery

2019 ◽  
Vol 11 (15) ◽  
pp. 1786 ◽  
Author(s):  
Tianyang Dong ◽  
Yuqi Shen ◽  
Jian Zhang ◽  
Yang Ye ◽  
Jing Fan
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Google Earth ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Tree Detection ◽  
Single Tree

High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.

scholarly journals SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images

2021 ◽  
Vol 13 (15) ◽  
pp. 2910
Author(s):  
Xiaolong Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Wentao Zheng ◽  
Hao Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Method ◽  
Detection Methods ◽  
Utilization Rate ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Deep Convolutional Neural Networks

Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.

scholarly journals Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 660
Author(s):  
Liqiong Chen ◽  
Wenxuan Shi ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Detection Speed

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.

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.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

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