Ship Targets Detection in Remote Sensing Images Based on Improved Faster-RCNN

Abstract An improved algorithm is proposed to solve the problems of inaccurate recognition and low recall of Faster-Regions with Convolutional Neural Network (Faster-RCNN) algorithm for the detection of ship targets in remote sensing images. The algorithm is based on the Faster-RCNN network framework. Aiming at the small size and dense distribution of ship targets in remote sensing images, the feature extraction network is improved to enhance the detection ability of small targets. ResNet50 is used as the basic feature extraction network of the algorithm,and the hole residual block is introduced for multi-layer feature fusion to construct a new feature extraction network,which improves the feature extraction capability of the algorithm. The experimental results show that compared with the Faster-RCNN algorithm, this algorithm can learn more abundant target features in smaller pixel areas, thereby effectively improving the detection accuracy of ship targets.

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Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

Remote Sensing ◽

10.3390/rs12203316 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3316 ◽

Cited By ~ 1

Author(s):

Yulian Zhang ◽

Lihong Guo ◽

Zengfa Wang ◽

Yang Yu ◽

Xinwei Liu ◽

...

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

Atmospheric Correction ◽

Recall Rate ◽

Google Earth ◽

Superior Performance ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Feature Maps ◽

Ship Detection

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

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Subtask Attention Based Object Detection in Remote Sensing Images

Remote Sensing ◽

10.3390/rs13101925 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1925

Author(s):

Shengzhou Xiong ◽

Yihua Tan ◽

Yansheng Li ◽

Cai Wen ◽

Pei Yan

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

Detection Task ◽

Feature Representation ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Attention Network ◽

Multi Scale ◽

Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

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Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images

Remote Sensing ◽

10.3390/rs13224597 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4597

Author(s):

Puhua Chen ◽

Lei Guo ◽

Xiangrong Zhang ◽

Kai Qin ◽

Wentao Ma ◽

...

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Urban Development ◽

Change Detection ◽

Information Fusion ◽

Feature Fusion ◽

Feature Learning ◽

Remote Sensing Images ◽

Imaging Processing ◽

Sensing Applications

Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be determined to estimate their influence. Additionally, by analyzing the sequential difference maps, the change tendency can be found to help to predict future changes, such as urban development and environmental pollution. The complex variety of changes and interferential changes caused by imaging processing, such as season, weather and sensors, are critical factors that affect the effectiveness of change detection methods. Recently, there have been many research achievements surrounding this topic, but a perfect solution to all the problems in change detection has not yet been achieved. In this paper, we mainly focus on reducing the influence of imaging processing through the deep neural network technique with limited labeled samples. The attention-guided Siamese fusion network is constructed based on one basic Siamese network for change detection. In contrast to common processing, besides high-level feature fusion, feature fusion is operated during the whole feature extraction process by using an attention information fusion module. This module can not only realize the information fusion of two feature extraction network branches, but also guide the feature learning network to focus on feature channels with high importance. Finally, extensive experiments were performed on three public datasets, which could verify the significance of information fusion and the guidance of the attention mechanism during feature learning in comparison with related methods.

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Detection of Schools in Remote Sensing Images Based on Attention-Guided Dense Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110736 ◽

2021 ◽

Vol 10 (11) ◽

pp. 736

Author(s):

Han Fu ◽

Xiangtao Fan ◽

Zhenzhen Yan ◽

Xiaoping Du

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

State Of The Art ◽

Feature Representation ◽

Detection Accuracy ◽

Dense Network ◽

Remote Sensing Images ◽

Composite Object ◽

Detection Algorithms

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.

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A Lightweight Object Detection Framework for Remote Sensing Images

Remote Sensing ◽

10.3390/rs13040683 ◽

2021 ◽

Vol 13 (4) ◽

pp. 683

Author(s):

Lang Huyan ◽

Yunpeng Bai ◽

Ying Li ◽

Dongmei Jiang ◽

Yanning Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Real Time ◽

Large Scale ◽

Feature Fusion ◽

Computational Cost ◽

Feature Representation ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Low Level

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

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Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network

Sensors ◽

10.3390/s20174696 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4696

Author(s):

Changqing Cao ◽

Jin Wu ◽

Xiaodong Zeng ◽

Zhejun Feng ◽

Ting Wang ◽

...

Keyword(s):

Remote Sensing ◽

Target Detection ◽

Aerial Images ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Detection Algorithms ◽

Wide Range ◽

Aerial Remote Sensing ◽

Small Targets ◽

Improved Algorithm

The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1–3%, effectively verifying the superiority of the improved algorithm.

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Research on Object Detection Algorithm Based on Multilayer Information Fusion

Mathematical Problems in Engineering ◽

10.1155/2020/9076857 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Bao-Yuan Chen ◽

Yu-Kun Shen ◽

Kun Sun

Keyword(s):

Feature Extraction ◽

Object Detection ◽

Feature Fusion ◽

Basic Feature ◽

Detection Algorithm ◽

Mean Average Precision ◽

Detection Accuracy ◽

Average Precision ◽

Position Information ◽

The Mean

At present, object detectors based on convolution neural networks generally rely on the last layer of features extracted by the feature extraction network. In the process of continuous convolution and pooling of deep features, the position information cannot be completely transferred backward. This paper proposes a multiscale feature reuse detection model, which includes the basic feature extraction network DenseNet, feature fusion network, multiscale anchor region proposal network, and classification and regression network. The fusion of high-dimensional features and low-dimensional features not only strengthens the model's sensitivity to objects of different sizes but also strengthens the transmission of information, so that the feature map has rich deep semantic information and shallow location information at the same time, which significantly improves the robustness and detection accuracy of the model. The algorithm is trained and tested in Pascal VOC2007 dataset. The experimental results show that the mean average precision of the objects in the dataset is 73.87%. At the same time, compared with the mainstream faster RCNN and SSD detection models, the mean average precision of object detection algorithm based on DenseNet is improved by 5.63% and 3.86%, respectively.

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Improved YOLO Network for Free-Angle Remote Sensing Target Detection

Remote Sensing ◽

10.3390/rs13112171 ◽

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.

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

Remote Sensing ◽

10.3390/rs13010071 ◽

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.

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

The Journal of Supercomputing ◽

10.1007/s11227-020-03604-4 ◽

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.

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