A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features

By Changren Zhu, Hui Zhou, Runsheng Wang, Jun Guo. IEEE Transactions on Geoscience and Remote Sensing, 2010

399 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/2c86351363c59d0f39f9f0296b149aee50047ee8

How researchers see this paper

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

In Aerospace Engineering

1. Zhenwei Shi
2. Gong Cheng
3. Junwei Han
4. Keyan Chen
5. Zhengxia Zou
6. Jieping Ye
7. Yuhong Guo
8. Lei Guo

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Peicheng Zhou
4. Zhenwei Shi
5. Lei Guo
6. Yiping Yang
7. Zikun Liu
8. Lubin Weng

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Zhenwei Shi
5. Lei Guo
6. Yiping Yang
7. Zikun Liu
8. Lubin Weng

Citations in the corpus

• Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine (2015, methodology): “Compared with SA-SVM and MF-SVM, the proposed approach achieves the best results in various weather conditions.”
• Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, background): “SIFT features [99], [33], [34], Haar features [14], Fisher vectors [33], [34], and statistical moments and their variations [30], [33], [34], [67],”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, neutral): “…multi-class object detection (Bai et al., 2014; Cheng et al., 2013b; Cheng et al., 2014a, b; Li and Itti, 2011), ship detection (Bi et al., 2012; Zhu et al., 2010), airport detection (Tao et al., 2011), airplane detection (Sun et al., 2012; Zhang et al., 2014b; Zhang et al., 2015b), weakly…”
• SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection (2018, background): “Zhu [1] extracts ships based on local binary patterns, shape features and gray intensity features.”
• Object Detection in 20 Years: A Survey (2019, methodology): “methods follow a two-stage detection paradigm: 1) candidate extraction and 2) target verification. In candidate extraction stage, some frequently used methods include gray value filtering based methods [383, 384], visual saliencybased methods [385–388], wavelet transform based methods [389], anomaly d”
• A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines (2017, background): “There are a wide array of ship recognition related applications including fishery management, smuggling activities, vessel traffic services and naval warfare (Proia and Page, 2010; Zhu et al., 2010).”
• Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature (2014, methodology): “[42] put forward a hierarchical method including shape analysis, feature extraction, and a semisupervised classification algorithm.”
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, background): “can be performed by learning a classifier, such as support vector machine (SVM) [1], [7], [8], [12], [13], [20]–[24], AdaBoost [2]–[5], k-nearest neighbors [15], [17], conditional random field [6], [19], and sparse-coding-based classifier [9]–[11], [14], [16], which captures the variation in object ”
• Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images (2014, methodology): “A number of recent works in literatures have proposed various methods for different object detection tasks [1-10].”
• Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds (2016, background): “SHIP detection in remote sensing images is an important issue and has wide applications such as fishery management, vessel traffic services, and naval warfare [1].”