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

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.

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

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.

scholarly journals A Lightweight Object Detection Framework for Remote Sensing Images

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.

scholarly journals IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Jiangqiao Yan ◽  
Hongqi Wang ◽  
Menglong Yan ◽  
Wenhui Diao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Performance ◽  
Candidate Region ◽  
Detection Accuracy ◽  
Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

scholarly journals Enhanced Feature Representation in Detection for Optical Remote Sensing Images

2019 ◽  
Vol 11 (18) ◽  
pp. 2095 ◽  
Author(s):  
Kun Fu ◽  
Zhuo Chen ◽  
Yue Zhang ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Computational Cost ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Two Stage ◽  
Multi Scale ◽  
One Stage

In recent years, deep learning has led to a remarkable breakthrough in object detection in remote sensing images. In practice, two-stage detectors perform well regarding detection accuracy but are slow. On the other hand, one-stage detectors integrate the detection pipeline of two-stage detectors to simplify the detection process, and are faster, but with lower detection accuracy. Enhancing the capability of feature representation may be a way to improve the detection accuracy of one-stage detectors. For this goal, this paper proposes a novel one-stage detector with enhanced capability of feature representation. The enhanced capability benefits from two proposed structures: dual top-down module and dense-connected inception module. The former efficiently utilizes multi-scale features from multiple layers of the backbone network. The latter both widens and deepens the network to enhance the ability of feature representation with limited extra computational cost. To evaluate the effectiveness of proposed structures, we conducted experiments on horizontal bounding box detection tasks on the challenging DOTA dataset and gained 73.49% mean Average Precision (mAP), achieving state-of-the-art performance. Furthermore, our method ran significantly faster than the best public two-stage detector on the DOTA dataset.

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

2020 ◽  
Vol 12 (20) ◽  
pp. 3316 ◽  
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.

scholarly journals An Oil Well Dataset Derived from Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (6) ◽  
pp. 1132
Author(s):  
Zhibao Wang ◽  
Lu Bai ◽  
Guangfu Song ◽  
Jie Zhang ◽  
Jinhua Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Google Earth ◽  
Oil Wells ◽  
Oil Well ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Remote Sensing Images

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

scholarly journals Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 3182
Author(s):  
Zheng He ◽  
Li Huang ◽  
Weijiang Zeng ◽  
Xining Zhang ◽  
Yongxin Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Large Scale ◽  
Small Scale ◽  
Detection Accuracy ◽  
Small Object ◽  
Direction Vector ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Hierarchical Scale

The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales.

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