scholarly journals Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

2021 ◽  
Vol 13 (11) ◽  
pp. 2207
Author(s):  
Fengcheng Ji ◽  
Dongping Ming ◽  
Beichen Zeng ◽  
Jiawei Yu ◽  
Yuanzhao Qing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Input Image ◽  
Majority Voting ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Public And Private ◽  
Detection Model ◽  
Private Datasets

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

scholarly journals Vehicle Detection in Remote Sensing Image Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liming Zhou ◽  
Chang Zheng ◽  
Haoxin Yan ◽  
Xianyu Zuo ◽  
Baojun Qiao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Small Target ◽  
Remote Sensing Images ◽  
Target Feature ◽  
Original Algorithm ◽  
Wide Range ◽  
Small Targets

Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.

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 Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8113
Author(s):  
Kun Fang ◽  
Jianquan Ouyang ◽  
Buwei Hu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Target Prediction ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Detection Model ◽  
Feature Information ◽  
Small Targets

Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

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