scholarly journals Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liming Zhou ◽  
Haoxin Yan ◽  
Chang Zheng ◽  
Xiaohan Rao ◽  
Yahui Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
High Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Aircraft Detection ◽  
Low Rate

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

scholarly journals Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4673-4687
Author(s):  
Jixiang Zhao ◽  
Shanwei Liu ◽  
Jianhua Wan ◽  
Muhammad Yasir ◽  
Huayu Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Evidence Theory ◽  
Remote Sensing Image

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 Jitter Detection Method Based on Sequence CMOS Images Captured by Rolling Shutter Mode for High-Resolution Remote Sensing Satellite

2022 ◽  
Vol 14 (2) ◽  
pp. 342
Author(s):  
Ying Zhu ◽  
Tingting Yang ◽  
Mi Wang ◽  
Hanyu Hong ◽  
Yaozong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Remote Sensing Data ◽  
Error Modeling ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Satellite Platform ◽  
Dense Matching ◽  
Remote Sensing Satellite

Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation.

Ship Detection Method in Remote Sensing Image Based on Feature Fusion

2020 ◽  
Vol 49 (7) ◽  
pp. 710004
Author(s):  
史文旭 Wen-xu SHI ◽  
江金洪 Jin-hong JIANG ◽  
鲍胜利 Sheng-li BAO
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Ship Detection

scholarly journals An Approach on Image Processing of Deep Learning Based on Improved SSD

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 495
Author(s):  
Liang Jin ◽  
Guodong Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Remote Sensing Image ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Image Detection ◽  
Ship Detection ◽  
Real Time Detection

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

scholarly journals DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 321-326 ◽  
Author(s):  
Y. Dai ◽  
J. S. Xiao ◽  
B. S. Yi ◽  
J. F. Lei ◽  
Z. Y. Du
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Detection Method ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aspect Ratios ◽  
Efficient Detection

Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.

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.

Sign in / Sign up

Export Citation Format

Share Document