Dr. Haitao Lang Profile

Dr. Haitao Lang

at Beijing Univ of Chemical Techn

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Author

Publications (10)

Proceedings Article | 19 October 2023 Presentation + Paper

Maritime ship tracking based on SAR constellation: a preliminary implementation

Haitao Lang, Lihui Niu, Jialu Li, Wenjing Li, Li Ma

Proceedings Volume 12733, 127330T (2023) https://doi.org/10.1117/12.2678817

KEYWORDS: Synthetic aperture radar, Satellites, Artificial intelligence, Image segmentation, Satellite imaging, Feature extraction, Earth observing sensors, Target detection, Maritime surveillance

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Proceedings Article | 9 October 2018 Paper

Distance metric learning for ship classification in SAR images

Yongjie Xu, Haitao Lang, Xiaopeng Chai, Li Ma

Proceedings Volume 10789, 107891C (2018) https://doi.org/10.1117/12.2324954

KEYWORDS: Synthetic aperture radar, Image classification, Mahalanobis distance, Feature extraction, Databases, Ocean optics, Distance measurement

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Proceedings Article | 5 October 2017 Paper

A comparative study on methods of improving SCR for ship detection in SAR image

Haitao Lang, Hongji Shi, Yunhong Tao, Li Ma

Proceedings Volume 10432, 104320M (2017) https://doi.org/10.1117/12.2277907

KEYWORDS: Synthetic aperture radar, Sensors, Polarization, Signal detection, Wavelets, Maritime surveillance, Scattering, Image fusion, Data modeling, Model-based design

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SPIE Journal Paper | 12 January 2017

Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery

Haitao Lang, Xingyao Zhang, Yuyang Xi, Xi Zhang, Wei Li

JARS, Vol. 11, Issue 01, 015006, (January 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.015006

KEYWORDS: Image fusion, Image segmentation, Synthetic aperture radar, Feature extraction, Binary data, Image classification, Image processing algorithms and systems, Image filtering, Neurons, Digital filtering

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In recent years, oil spill surveillance with space-borne synthetic aperture radar (SAR) has received unprecedented attention and has been gradually developed into a common technique for maritime environment protection. A typical SAR-based oil spill detection process consists of three steps: (1) dark-spot segmentation, (2) feature extraction, and (3) oil spill and look-alike discrimination. As a preliminary task in the oil spill detection process chain, dark-spot segmentation is a critical and fundamental step prior to feature extraction and classification, since its output has a direct impact on the two subsequent stages. The balance between the detection probability and false alarm probability has a vital impact on the performance of the entire detection system. Unfortunately, this problem has not drawn as much attention as the other two stages. A specific effort has been placed on dark-spot segmentation in single-pol SAR imagery. A combination of fine designed features, including gray features, geometric features, and textural features, is proposed to characterize the oil spill and seawater for improving the performance of dark-spot segmentation. In the proposed process chain, a histogram stretching transform is incorporated before the gray feature extraction to enhance the contrast between possible oil spills and water. A simple but effective multiple-level thresholding algorithm is developed to conduct a binary classification before the geometric feature extraction to obtain more accurate area features. A local binary pattern code is computed and assigned as the textural feature for a pixel to characterize the physical difference between oil spills and water. The experimental result confirms that the proposed fine designed feature combination outperforms existing approaches in both aspects of overall segmentation accuracy and the capability to balance detection probability and false alarm probability. It is a promising alternative that can be incorporated into existing oil spill detection systems to further improve system performance.

Proceedings Article | 18 October 2016 Presentation + Paper

Capability of geometric features to classify ships in SAR imagery

Haitao Lang, Siwen Wu, Quan Lai, Li Ma

Proceedings Volume 10004, 1000415 (2016) https://doi.org/10.1117/12.2241375

KEYWORDS: Synthetic aperture radar, Image classification, Feature extraction, Image resolution, Image segmentation, Polarimetry, Scattering, Environmental sensing, Neural networks, Binary data

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Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f₁), width (f₂), area (f₃), perimeter (f₄), elongatedness (f₅) and compactness (f₆). Among them, two basic features, length (f₁) and width (f₂), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

SPIE Journal Paper | 22 May 2014

Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery

Haitao Lang, Jie Zhang, Ting Zhang, Di Zhao, Junmin Meng

JARS, Vol. 8, Issue 01, 083623, (May 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083623

KEYWORDS: Synthetic aperture radar, Polarimetry, Stanford Linear Collider, Databases, Detection and tracking algorithms, Target detection, Image classification, Remote sensing, Fuzzy logic, Polarization

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Proceedings Article | 3 August 2010 Paper

One-shot in-line digital-holography-based two-dimensional Hilbert demodulation technique

Weiqing Pan, Yongjian Zhu, Yufeng Fan, Haitao Lang

Proceedings Volume 7790, 779016 (2010) https://doi.org/10.1117/12.860238

KEYWORDS: Digital holography, Holograms, 3D image reconstruction, Phase shifts, Digital imaging, Demodulation, Digital recording, Charge-coupled devices, CCD image sensors, Holography

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Proceedings Article | 7 September 2006 Paper

Design of coded aperture arrays by means of a global optimization algorithm

Haitao Lang, Liren Liu, Qingguo Yang

Proceedings Volume 6294, 62940S (2006) https://doi.org/10.1117/12.678278

KEYWORDS: Optimization (mathematics), Simulation of CCA and DLA aggregates, Coded apertures, Coded aperture imaging, Algorithm development, Signal to noise ratio, Mathematical modeling, Binary data, MATLAB, Cameras

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Proceedings Article | 30 August 2005 Paper

A three-dimensional imaging method based on the principle of coded aperture imaging

Haitao Lang, Liren Liu, Qingguo Yang

Proceedings Volume 5896, 58960K (2005) https://doi.org/10.1117/12.612667

KEYWORDS: Cameras, Coded aperture imaging, 3D image processing, Computer programming, 3D image reconstruction, Photography, Image filtering, Image processing, Imaging arrays, X-ray imaging

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Proceedings Article | 24 August 2005 Paper

Defocus transfer function for circularly symmetric pupils under polychromatic illumination

Qingguo Yang, Liren Liu, Haitao Lang

Proceedings Volume 5896, 58960L (2005) https://doi.org/10.1117/12.612585

KEYWORDS: Optical transfer functions, Atrial fibrillation, Wigner distribution functions, Chromatic aberrations, Spatial frequencies, Imaging systems, Receivers, Optical design, Sensors, Optical filters

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Showing 5 of 10 publications

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