Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images

By Ke Li, Gong Cheng, Shuhui Bu, Xiong You. IEEE Transactions on Geoscience and Remote Sensing, 2018

387 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3c0aaba5be7693c37458d0ec35e41a39359b700a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Chongyi Li
4. Xiaoqiang Lu
5. Runmin Cong
6. Ke Li
7. Sam Kwong
8. Junhui Hou

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Ke Li
5. Runmin Cong
6. Yuan Yuan
7. Yachuang Feng
8. Junhui Hou

Citations in the corpus

• Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images (2019, background): “have many different orientations, scales, and types since the RSIs are taken overhead [13], [14].”
• CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery (2019, methodology): “Since NWPU-VHR10 does not specify the partition of training and testing sets, we randomly choose 75% of the positive images as a training set and the rest positive images as a test set by following the widely adopted partition scheme [50], except that we do not include any negative images for traini”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, methodology): “This dataset has been widely used in the earth observation community (Cheng et al., 2014; Cheng et al., 2018b; Farooq et al., 2017; Guo et al., 2018; Han et al., 2017a; Li et al., 2018; Yang et al., 2018b; Yang et al., 2017; Zhong et al., 2018).”
• Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection (2019, methodology): “Recently, a novel method dealing with contextual information as well as the rotation is proposed [47].”
• EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “The optical datasets include DIOR [61], DOTA [62], FAIR1M [63], HRRSD [64], NWPUVHR10 [65], RSOD [85], UCAS-AOD [86], and VisDrone [87].”
• SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects (2018, neutral): “es the image cascade network strucMethod mAP R-P-Faster R-CNN [14] 76.50 SSD512 [26] 78.40 DSSD321 [10] 78.80 DSOD300 [33] 79.80 Deformable R-FCN [40] 79.10 Deformable Faster R-CNN [32] 84.40 RICADet [22] 87.12 RDAS512 [3] 89.50 Multi-Scale CNN [13] 89.60 SCRDet (proposed) 91.75 Table 4: Performance”
• Object Detection Algorithm Based on Improved YOLOv3 (2020, methodology): “It has been widely used in autonomous driving [2], pedestrian detection [3], medical imaging [4], industrial detection [5], robot vision [6], intelligent video surveillance [7], remote sensing images [8], etc.”