Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images

Fu, Kun; Chang, Zhonghan; Zhang, Yue; Xu, Guangluan; Zhang, Keshu; Sun, Xian

doi:10.1016/j.isprsjprs.2020.01.025

Cited by 207 publications

(85 citation statements)

References 30 publications

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“…Deep learning is widely used in environmental remote sensing, such as land use extraction, land cover change analysis [5,6], remote sensing image classification [7,8], and object detection [9][10][11]. The deep-learning models commonly used in road extraction are convolutional neural networks (CNNs) [12], whose network structure is often used for various computer-vision tasks, and semantic segmentation technology [13][14][15][16][17][18] is another area of great research interest in image interpretation.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Lin

Wang

et al. 2020

Remote Sensing

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Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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Section: Introductionmentioning

confidence: 99%

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Lin

Wang

et al. 2020

Remote Sensing

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“…An improved Faster R-CNN based on maximum stability extremal region decision criterion for SAR ship detection in harbor was proposed in [35], aiming to achieve effective inshore ship detection. To improve the ship detection accuracy, much efforts have been devoted from different perspectives to improving the ship detection accuracy, such as sea-land segmentation [36], contextual information fusion [37], attention mechanism [43]- [46], oriented proposal [38], [39] and transfer learning [47]- [49].…”

Section: Introductionmentioning

confidence: 99%

Efficient Low-Cost Ship Detection for SAR Imagery Based on Simplified U-Net

Mao

Yang

et al. 2020

IEEE Access

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Due to the rapid development of chip technology and deep learning revolution, many ship detection frameworks for synthetic aperture radar (SAR) imagery based on convolutional neural networks (CNNs) have been proposed and achieved great success. However, there are problems hampering their development: 1) For the SAR ship detection task, it is uneconomic to apply heavy backbone network to extract features because it results in heavy computing load and prolongs the inference time cost; 2) The anchor-based methods usually have massive hyper-parameters, which typically need to be tuned carefully and easily lead to weak detection performance. To alleviate the problems, an efficient low-cost ship detection network for SAR imagery is proposed in this paper. Firstly, a simplified U-Net as the backbone to extract features is proposed. It only contains ∼ 0.47 million learnable weights, which is 2.37%, 0.76%, 0.34%, 1.01%, 0.55% and 1.07% of DarkNet-19, DarkNet-53, VGG-16, ResNet-50, ResNet-101 and ResNext-101, respectively. Secondly, an anchor-free SAR ship detection framework consisting of a bounding boxes regression sub-net and a score map regression sub-net based on simplified U-Net is proposed. To evaluate the effectiveness of our method, extensive experiments have been conducted and a more comprehensive set of evaluation metrics have been applied. Results demonstrate that the proposed network achieves 68.1% average precision and 67.6% average recall on the SAR ship detection dataset (SSDD), respectively. Compared with the state-of-the-art works, our proposed network achieves very competitive detection performance and extreme lightweight (∼ 0.93 million learnable weights in total).

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“…Ding et al [20] took the geometry transformation between horizontal and rotational RoIs into account and developed a lightweight Region of Interest (RoI) Transformer for rotation-invariant region feature extraction. Following the rotational regional proposal network (RRPN) [21], many researchers incorporate rotation-aware factors into a regional proposal network (RPN) to handle object rotation variations [22][23][24]. Specifically, Li et al [25] embedded additional multi-angle anchors into RPN for the generation of multi-scale and translation-invariant candidate regions.…”

Section: Introductionmentioning

confidence: 99%

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

Xiong

et al. 2020

Remote Sensing

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Object detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. Although a lot of methods have been developed to address this problem, many of them cannot fully exploit multilevel context information or handle cluttered background in RS images either. To this end, in this paper, we propose a feature fusion and filtration network (F3-Net) to improve object detection in RS images, which has higher capacity of combining the context information at multiple scales while suppressing the interference from the background. Specifically, F3-Net leverages a feature adaptation block with a residual structure to adjust the backbone network in an end-to-end manner, better considering the characteristics of RS images. Afterward, the network learns the context information of the object at multiple scales by hierarchically fusing the feature maps from different layers. In order to suppress the interference from cluttered background, the fused feature is then projected into a low-dimensional subspace by an additional feature filtration module. As a result, more relevant and accurate context information is extracted for further detection. Extensive experiments on DOTA, NWPU VHR-10, and UCAS AOD datasets demonstrate that the proposed detector achieves very promising detection performance.

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Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images

Cited by 207 publications

References 30 publications

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Efficient Low-Cost Ship Detection for SAR Imagery Based on Simplified U-Net

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

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