scholarly journals A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 319
Author(s):  
Xin Chen ◽  
Jinghong Liu ◽  
Fang Xu ◽  
Zhihua Xie ◽  
Yujia Zuo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Identification ◽  
False Alarms ◽  
Rotation Invariant ◽  
Detection Scheme ◽  
Invariant Feature ◽  
Complex Background ◽  
Bounding Boxes ◽  
The Impact ◽  
Aircraft Detection

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

scholarly journals Object Detection Based on Global-Local Saliency Constraint in Aerial Images

2020 ◽  
Vol 12 (9) ◽  
pp. 1435 ◽  
Author(s):  
Chengyuan Li ◽  
Bin Luo ◽  
Hailong Hong ◽  
Xin Su ◽  
Yajun Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Semantic Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Natural Image ◽  
Optical Remote Sensing ◽  
Complex Background ◽  
Bounding Boxes ◽  
The Impact ◽  
Oriented Bounding Boxes

Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.

scholarly journals Rotation-Invariant Feature Learning for Object Detection in VHR Optical Remote Sensing Images by Double-Net

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20818-20827 ◽  
Author(s):  
Zhi Zhang ◽  
Ruoqiao Jiang ◽  
Shaohui Mei ◽  
Shun Zhang ◽  
Yifan Zhang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Learning ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Rotation Invariant ◽  
Invariant Feature

scholarly journals ORIENTED VEHICLE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON FEATURE AMPLIFICATION AND CATEGORY BALANCE BY OVERSAMPLING DATA AUGMENTATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 153-159
Author(s):  
N. Mo ◽  
L. Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Vehicle Detection ◽  
Training Dataset ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Fine Grained ◽  
Bounding Boxes ◽  
The Impact ◽  
Oriented Bounding Boxes

Abstract. Vehicles usually lack detailed information and are difficult to be trained on the high-resolution remote sensing images because of small size. In addition, vehicles contain multiple fine-grained categories that are slightly different, randomly located and oriented. Therefore, it is difficult to locate and identify these fine categories of vehicles. Considering the above problems in high-resolution remote sensing images, this paper proposes an oriented vehicle detection approach. First of all, we propose an oversampling and stitching method to augment the training dataset by increasing the frequency of objects with fewer training samples in order to balance the number of objects in each fine-grained vehicle category. Then considering the effect of the pooling operations on representing small objects, we propose to improve the resolution of feature maps so that detailed information hidden in feature maps can be enriched and they can better distinguish the fine-grained vehicle categories. Finally, we design a joint training loss function for horizontal and oriented bounding boxes with center loss, to decrease the impact of small between-class diversity on vehicle detection. Experimental verification is performed on the VEDAI dataset consisting of 9 fine-grained vehicle categories so as to evaluate the proposed framework. The experimental results show that the proposed framework performs better than most of competitive approaches in terms of a mean average precision of 60.7% and 60.4% in detecting horizontal and oriented bounding boxes respectively.

scholarly journals A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

2020 ◽  
Vol 12 (2) ◽  
pp. 246 ◽  
Author(s):  
Yue Wu ◽  
Wenping Ma ◽  
Maoguo Gong ◽  
Zhuangfei Bai ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection Methods ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Detection Strategy ◽  
Image Pixels ◽  
Bounding Boxes ◽  
Coarse To Fine

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.

scholarly journals A Fast Aircraft Detection Method for SAR Images Based on Efficient Bidirectional Path Aggregated Attention Network

2021 ◽  
Vol 13 (15) ◽  
pp. 2940
Author(s):  
Ru Luo ◽  
Lifu Chen ◽  
Jin Xing ◽  
Zhihui Yuan ◽  
Siyu Tan ◽  
...  
Keyword(s):  
Spatial Information ◽  
Speckle Noise ◽  
False Alarms ◽  
Detection Accuracy ◽  
Sar Images ◽  
Attention Network ◽  
Multi Scale ◽  
Complex Background ◽  
Size Heterogeneity ◽  
Aircraft Detection

In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed.

Method of Aircraft Target Detection in Remote Sensing Images Based on Rotation-invariant Feature

2019 ◽  
Vol 48 (6) ◽  
pp. 610002
Author(s):  
林亿 LIN Yi ◽  
赵明 ZHAO Ming ◽  
潘胜达 PAN Sheng-da ◽  
安博文 AN Bo-wen
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Remote Sensing Images ◽  
Rotation Invariant ◽  
Invariant Feature

SCALE INVARIANT FEATURE MATCHING USING ROTATION-INVARIANT DISTANCE FOR REMOTE SENSING IMAGE REGISTRATION

2013 ◽  
Vol 27 (02) ◽  
pp. 1354004 ◽  
Author(s):  
QIAOLIANG LI ◽  
HUISHENG ZHANG ◽  
TIANFU WANG
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Invariant Feature ◽  
Match Rate ◽  
Invariant Distance ◽  
Significant Difference ◽  
Scale Invariant Feature

Scale invariant feature transform (SIFT) has been widely used in image matching. But when SIFT is introduced in the registration of remote sensing images, the keypoint pairs which are expected to be matched are often assigned two different value of main orientation owing to the significant difference in the image intensity between remote sensing image pairs, and therefore a lot of incorrect matches of keypoints will appear. This paper presents a method using rotation-invariant distance instead of Euclid distance to match the scale invariant feature vectors associated with the keypoints. In the proposed method, the feature vectors are reorganized into feature matrices, and fast Fourier transform (FFT) is introduced to compute the rotation-invariant distance between the matrices. Much more correct matches are obtained by the proposed method since the rotation-invariant distance is independent of the main orientation of the keypoints. Experimental results indicate that the proposed method improves the match performance compared to other state-of-art methods in terms of correct match rate and aligning accuracy.

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