Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds

By Zikun Liu, Hongzhen Wang, Lubin Weng, Yiping Yang. IEEE Geoscience and Remote Sensing Letters, 2016

385 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/28200394c180d1e42e65ff3a9ddef02475448a5a

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. Junwei Han
2. Gong Cheng
3. Guisong Xia
4. Liangpei Zhang
5. Xiang Bai
6. Dacheng Tao
7. Luc Van Gool
8. Jianke Zhu

In Media Technology

1. Guisong Xia
2. Gong Cheng
3. Junwei Han
4. Liangpei Zhang
5. Gui-Song Xia
6. Xiwen Yao
7. Jian Ding
8. Xiang Bai

Citations in the corpus

• Tiny Object Detection in Aerial Images (2021, background): “In the past years, numerous object detection datasets in aerial images, such as NWPU VHR-10 [16], HRSC2016 [17], VEDAI [18], xView [19], DOTA [1], VisDrone [20], UAVDT [21], and DIOR [3] have been proposed to make advancement in object detection research for Earth Vision.”
• Oriented R-CNN for Object Detection (2021, methodology): “For the detection accuracy on the HRSC2016, we adopt the mean average precision (mAP) as evaluation criteria, which is consistent with PASCAL VOC 2007 [8] and VOC 2012.”
• SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery (2022, background): “Currently, most object detection techniques are solely designed and applied for a single modality, such as red-green-blue (RGB) and infrared (IR) [12], [13].”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, methodology): “…object detection datasets including (Benedek et al., 2011; Cheng and Han, 2016; Cheng et al., 2016a; Heitz and Koller, 2008; Liu and Mattyus, 2015; Liu et al., 2016b; Razakarivony and Jurie, 2015; Tanner et al., 2009; Xia et al., 2018; Xiao et al., 2015; Zhu et al., 2015a), the proposed DIOR…”
• Vessel detection and classification from spaceborne optical images: A literature survey (2018, methodology): “…deal with “inshore vessel detection” in ports and in inland waters (Beşbinar and Alatan, 2015; He et al., 2017; Hu et al., 2015; S. Li, Zhou, 2016; B. Liu et al., 2016; Liu et al., 2014; Ren et al., 2015; J. Xu et al., 2014), which, as opposed to vessel detection on open sea, has to cope with high…”
• Poly Kernel Inception Network for Remote Sensing Detection (2024, background): “The challenges faced by remote sensing object detection primarily stem from objects with arbitrary orientations and substantial scale variations [3, 11, 40, 56, 64, 75].”
• Learning RoI Transformer for Oriented Object Detection in Aerial Images (2019, methodology): “These methods have demonstrated promising potentials on detecting sparsely distributed objects [26, 43, 27, 30].”
• Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection (2019, methodology): “Some detection results of the proposed method on HRSC2016 [36] in (a), MSRA-TD500 [37] in (b-c), and RCTW-17 [39] in (d-e).”
• FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery (2021, neutral): “However, the images in HRSC2016 only cover a very limited number of ports and the number of ships are limited.”
• Align Deep Features for Oriented Object Detection (2020, methodology): “Some methods [2], [22], [23] adopt rotated anchors with different angles, scales, and aspect ratios to alleviate this issue, while involving heavy computations related to anchors (e.”
• RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image (2018, methodology): “Liu et al. [5] takes the object proposals generated by SRBBS [6] then Fast R-CNN is carried out on each proposal region.”
• DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (2017, background): “Extensive studies have been devoted to object detection in aerial images [24, 15, 18, 3, 20, 39, 19, 32, 31, 22], drawing upon recent advances in Computer Vision and accounting for the high demands of Earth Vision applications.”