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4th International Conference on Intelligent Control and Information processing (ICICIP)

作者： Xie, Qinchuan Lang, Wenhui Zhang, Xi Yang, Xuezhi Signal and information processing and focuses on remote sensing image processing|c|

ISBN: (纸本)9781467362498;9781467362481

The benefits of combined C-band with L band polarimetric quantities for supervised sea ice classification in the Eastern Weddell Sea, Antarctica, are investigated. In experiments, we compared the performance of a muimum likelihood classifier when used with the combined preferred polarimetric parameter sets against using the individual preferred polarimetric parameter sets. The relationship between classification accuracy and preferred number of polarimetric parameters to use for classification was examined, as well as whether locally linear embedding (LLE)[1] can be used to reduce the dimensionality of the parameter set. Combining dual-frequency polarimetric quantities often improves classifier accuracy against using individual single-frequencies. With the increase of dimensionality of the preferred polarimetric parameter set, the classificati-on using high-dimensionality results in improvements over the smaller subsets or does not make a statistically significant difference. Using all available polarimetric quantities is recommended over the test site, data fusion with locally linear embedding (LLE) does not offer any benefit for sea ice classification.

关键词： data reduction geophysical signal processing maximum likelihood estimation oceanographic regions oceanographic techniques radar polarimetry remote sensing by radar sea ice sensor fusion signal classification

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Characterization of anomaly detection in hyperspectral imagery

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Sensor Review 2006年第2期26卷 137-146页

作者： Chang, Chein-I Hsueh, Mingkai Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

Purpose - The paper aims to characterize anomaly detection in hyperspectral imagery. Design/methodology/approach - This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image-based computer simulations and real image experiments to substantiate the work proposed in this paper. Findings - This paper developed an ACAD and custom-designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are - first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation. Originality/value - This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image-based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

关键词： image processing

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Kalman filtering approach to multispectral/hyperspectral image classification

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1999年第1期35卷 319-330页

作者： Chang, CI Brumbley, C Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Defense Fort Meade MD USA

Linear unmixing is a widely used remote sensing image processing technique for subpixel classification and detection where a scene pixel is generally modeled by a linear mixture of spectral signatures of materials present within the pixel. tin approach, called linear unmixing Kalman filtering (LUKF), is presented which incorporates the concept of linear unmixing into Kalman filtering so as to achieve signature abundance estimation, subpixel detection and classification for remotely sensed images. Zn this case, the linear mixture model used in linear unmixing is implemented as the measurement equation in Kalman filtering. The state equation which is required for Kalman filtering but absent in linear unmixing is then used to model the signature abundance. By utilizing these two equations the proposed LUKF not only can detect abrupt change in various signature abundances within pixels, but also can detect and classify desired target signatures. The performance of effectiveness and robustness of the LUKF is demonstrated through simulated data and real scene images, Satellite Pour l'Observation de la Terra (SPOT) and Hyperspectral Digital imagery Collection (HYDICE) data.

关键词： Kalman filters Filtering Hyperspectral imaging Nonlinear filters Equations Hyperspectral sensors Layout Pixel remote sensing image processing

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Hyperspectral Imaging 1

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1000年

作者： Chein-I Chang

ISBN: (数字)9781441991706

ISBN: (纸本)9780306474835

is an outgrowth of the research conducted over the years in the remote sensing signal and image processing laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

关键词： image processing and Computer Vision Atmospheric Sciences remote sensing/Photogrammetry Ecotoxicology Imaging / Radiology

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Multiple-window anomaly detection for hyperspectral imagery

Multiple-window anomaly detection for hyperspectral imagery

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2008 IEEE International Geoscience and remote sensing Symposium - Proceedings

作者： Liu, Wei-Min Chang, Chein-I Remote Sensing Signal Image Processing Laboratory Dept. of CSEE University of Maryland Baltimore County United States

ISBN: (纸本)9781424428083

Anomaly detection is of particular interest in hyperspectral image analysis since many unknown and subtle signals which cannot be resolved by multispectral sensors can now be uncovered by hyperspectral imagers. More importantly, the signals of this type generally cannot be identified by visual assessment or prior knowledge and provide crucial and critical information for data analysis. Many anomaly detectors have been designed based on the most widely used anomaly detector developed by Reed and Yu, called RX detector (RXD). However, a key issue in making RX detector-like anomaly detectors successful is how to effectively utilize the information provided by the sample correlation, e.g., sample covariance matrix used by RXD. This paper develops a concept of designing anomaly detectors which includes RXD-like anomaly detectors as special cases. It is referred to as multiple-window anomaly detection (MWAD) which makes use of multiple windows with varying sizes to capture different levels of local spectral variations so that anomalous targets of various sizes can be characterized and interpreted by different window sizes. With this new MWAD, many interesting findings can be derived including the RXD-like anomaly detectors as its special cases. © 2008 IEEE.

关键词： Covariance matrix

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SEGMENTATION OF A HIGH-RESOLUTION URBAN SCENE USING TEXTURE OPERATORS

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COMPUTER VISION GRAPHICS AND image processing 1984年第3期25卷 273-310页

作者： CONNERS, RW TRIVEDI, MM HARLOW, CA Remote Sensing and Image Processing Laboratory Department of Electrical and Computer Engineering Louisiana State University Baton Rouge Louisiana 70803

A study aimed at segmenting a high-resolution black and white image of Sunnyvale, California, is described. In this study regions were classified as belonging to any one of nine classes: residential, commercial/industrial, mobile home, water, dry land, runway/taxiway, aircraft parking, multilane highway, and vehicle parking. The classes were selected so that they directly relate to the Defense Mapping Agency's Mapping, Charting and Geodesy tangible features. To attack the problem a statistical segmentation procedure was devised. The primitive operators used to drive the segmentation are texture measures derived from cooccurrence matrices. The segmentation procedure considers three kinds of regions at each level of the segmentation: uniform, boundary, and unspecified. At every level the procedure differentiates uniform regions from boundary and unspecified regions. In the assigns a class label to the uniform regions. The boundary and unspecified regions are split to form higher level regions. The methodologies involved are mathematically developed as a series of hypothesis tests. While only a one-level segmentation was performed studies are described which show the capabilities of each of these hypothesis tests. In particular an 83% correct classification was obtained in testing the labeling procedure. These studies indicate that the proposed procedure should be useful for land use classifications as well as other problems.

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ML parameter estimation and minimum distance classifier for texture analysis using wavelet transform

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COMPUTERS & INDUSTRIAL ENGINEERING 1997年第1-2期33卷 449-452页

作者： Singh, R Singh, R Vasquez, R Laboratory for Applied Remote Sensing and Image Processing Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez Campus PR 06681-5000 Puerto Rico

This work concentrates on comparing the performance of the minimum distance classifier and maximum-likelihood classifier for texture analysis. A tree-structured wavelet transform has been used for extracting the features and the comparison is based on the correct classification percentage. The results indicate that the maximum-likelihood classifier performs marginally better than the mahalanobis distance for some feature sets. The Euclidean distance did not prove to be powerful in distinguishing the textures. The performance of various orthogonal wavelet transforms have also been compared in order to find out the best wavelet for each of the classifiers considered. (C) 1997 Elsevier Science Ltd.

关键词： texture analysis ML parameter estimation minimum distance wavelet transform

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Camera localization for augmented reality and indoor positioning:a vision-based 3D feature database approach

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International Journal of Digital Earth 2020年第6期13卷 727-741页

作者： Jing Li Chengyi Wang Xuejie Kang Qiang Zhao Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China Laboratory of Remote Sensing Image Processing Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China

The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality(AR),robotics,indoor GIS and *** localization is often a key and enabling technology among these *** this paper,we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGBD SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera ***,an AR registration method tightly coupled with a game engine is proposed,which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated *** experimental results show that the localization accuracy can achieve an average error of 35 cm based on a fine-tuned prior 3D feature database at 3 cm accuracy compared against the ground-truth 3D LiDAR *** influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment.

关键词： Augmented reality(AR) virtual geographic environment(VGE) indoor positioning indoor GIS LiDAR 3D reconstruction localization camera pose

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Does an endmember set really yield maximum simplex volume?

Does an endmember set really yield maximum simplex volume?

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2007 IEEE International Geoscience and remote sensing Symposium, IGARSS 2007

作者： Wu, Chao-Cheng Chang, Chein-I IEEE Department of Computer Science Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD United States

ISBN: (纸本)1424412129

One of commonly used criteria for finding an endmember set is to assume that for a given number of endmembers, p, ap-vertex simplex with its vertices specified byp endmembers always yields the maximum volume. Since there are also other criteria which have been widely used for endmember extraction, the issue of interest is "does an endmember set really produce a simplex with maximum volume?" In other words, using the criterion of a simplex with maximum volume is a better and more effective measure than other criteria currently being used by endmember extraction such as orthogonal projection-based pixel purity index (PPI), fully constrained least squares-based spectral unmixing, etc. This paper explores this issue by investigating a number of popular endmember extraction algorithms which are designed by different criteria. An extensive experiment-based study is also conducted for comparative analysis. © 2007 IEEE.

关键词： remote sensing

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

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2004 International Conference on image processing, ICIP 2004

作者： Chen, Yuan Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as Texture Unit Coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the MiniMammographic Database provided by the Mammographie image Analysis Society (MIAS) is used for experiments. ©2004 IEEE.

关键词： Mammography

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