Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

doi:10.3390/rs10010132

Cited by 473 publications

(349 citation statements)

References 34 publications

Supporting

Mentioning

321

Contrasting

Order By: Relevance

“…The SRBBS [8] is difficult to be embedded in the neural network, which would cost extra time for rotated proposal generation. The [9,12,21,27] used a design of rotated anchor in RPN [15]. However, the design is still time-consuming due to the dramatic increase in the number of anchors (num scales × num aspect ratios × num angles).…”

Section: Oriented Bounding Box Regressionmentioning

confidence: 99%

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Ding

Xue

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

945

661

View full text Add to dashboard Cite

Object detection in aerial images is an active yet challenging task in computer vision because of the birdview perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. Although rotated anchors have been used to tackle this problem, the design of them always multiplies the number of anchors and dramatically increases the computational complexity. In this paper, we propose a RoI Transformer to address these problems. More precisely, to improve the quality of region proposals, we first designed a Rotated RoI (RRoI) learner to transform a Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI). Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align (RPS-RoI-Align) module to extract rotation-invariant features from them for boosting subsequent classification and regression. Our RoI Transformer is with light weight and can be easily embedded into detectors for oriented object detection. A simple implementation of the RoI Transformer has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer. The results demonstrate that it can be easily integrated with other detector architectures and significantly improve the performances.

show abstract

Section: Oriented Bounding Box Regressionmentioning

confidence: 99%

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Ding

Xue

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

945

661

View full text Add to dashboard Cite

show abstract

“…To effectively evaluate the performances of the proposed method (FRIFB), we make a comparison with some stateof-the-art algorithms: BOW-SVM [13], rotation-aware features [14], exemplar-SVMs [15], COPD [12], R-DFPN [10], rotation-invariant CNN (RICNN) [3], you only look once (YOLO2) 1 [16], FPGM [8]. Similarly to RICNN, data augmentation by rotating or translating the training samples with various angles are performed in all compared methods.…”

Section: Comparison With State-of-the-art Algorithmsmentioning

confidence: 99%

Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Hong

Chanussot

et al. 2020

IEEE Geosci. Remote Sensing Lett.

125

View full text Add to dashboard Cite

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods.

show abstract

“…In recent years, Convolutional Neural Networks (CNNs) have been exploited for object detection in remote sensing images and promising results have been obtained [19], [28]- [32]. For example, [29] uses multi-scale CNN features for airport detection, [31] takes a weakly supervised learning framework for aircraft detection using CNNs, [30] and [32] integrate Dense Feature Pyramid Network (DFPN) and rotation regression for ship detection in remote sensing images, and [28] uses CNN features for multiclass object detection in remote sensing images.…”

Section: B Object Detection In Optical Remote Sensing Imagesmentioning

confidence: 99%

CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery

Zhang

2019

IEEE Trans. Geosci. Remote Sensing

369

102

View full text Add to dashboard Cite

Accurate and robust detection of multi-class objects in optical remote sensing images is essential to many real-world applications such as urban planning, traffic control, searching and rescuing, etc. However, state-of-the-art object detection techniques designed for images captured using ground-level sensors usually experience a sharp performance drop when directly applied to remote sensing images, largely due to the object appearance differences in remote sensing images in term of sparse texture, low contrast, arbitrary orientations, large scale variations, etc. This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images. The proposed CAD-Net learns global and local contexts of objects by capturing their correlations with the global scene (at scene-level) and the local neighboring objects or features (at object-level), respectively. In addition, it designs a spatial-and-scale-aware attention module that guides the network to focus on more informative regions and features as well as more appropriate feature scales. Experiments over two publicly available object detection datasets for remote sensing images demonstrate that the proposed CAD-Net achieves superior detection performance. The implementation codes will be made publicly available for facilitating future researches.

show abstract

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

Cited by 473 publications

References 34 publications

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery

Contact Info

Product

Resources

About