A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images

Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively.

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Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images

Remote Sensing ◽

10.3390/rs13183622 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3622

Author(s):

Xu He ◽

Shiping Ma ◽

Linyuan He ◽

Le Ru ◽

Chen Wang

Keyword(s):

Remote Sensing ◽

Aspect Ratio ◽

Object Detection ◽

Large Scale ◽

Large Aspect Ratio ◽

Remote Sensing Images ◽

Orientation Error ◽

Multi Scale ◽

Half Axis ◽

Oriented Object

Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.

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Self-Adaptive Aspect Ratio Anchor for Oriented Object Detection in Remote Sensing Images

Remote Sensing ◽

10.3390/rs13071318 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1318

Author(s):

Jie-Bo Hou ◽

Xiaobin Zhu ◽

Xu-Cheng Yin

Keyword(s):

Remote Sensing ◽

Aspect Ratio ◽

Object Detection ◽

Detection Methods ◽

Remote Sensing Images ◽

Feature Maps ◽

Aspect Ratios ◽

Feature Pyramid ◽

Oriented Object ◽

Self Adaptive

Object detection is a significant and challenging problem in the study of remote sensing. Since remote sensing images are typically captured with a bird’s-eye view, the aspect ratios of objects in the same category may obey a Gaussian distribution. Generally, existing object detection methods ignore exploring the distribution character of aspect ratios for improving performance in remote sensing tasks. In this paper, we propose a novel Self-Adaptive Aspect Ratio Anchor (SARA) to explicitly explore aspect ratio variations of objects in remote sensing images. To be concrete, our SARA can self-adaptively learn an appropriate aspect ratio for each category. In this way, we can only utilize a simple squared anchor (related to the strides of feature maps in Feature Pyramid Networks) to regress objects in various aspect ratios. Finally, we adopt an Oriented Box Decoder (OBD) to align the feature maps and encode the orientation information of oriented objects. Our method achieves a promising mAP value of 79.91% on the DOTA dataset.

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A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs13030433 ◽

2021 ◽

Vol 13 (3) ◽

pp. 433

Author(s):

Junge Shen ◽

Tong Zhang ◽

Yichen Wang ◽

Ruxin Wang ◽

Qi Wang ◽

...

Keyword(s):

Remote Sensing ◽

Feature Representation ◽

Dual Model ◽

Scene Classification ◽

Remote Sensing Images ◽

Single Model ◽

Fusion Strategy ◽

Multi Scale ◽

The Arts ◽

Scene Representation

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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