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作者： Helder, Dennis L. Markham, Brian L. Thome, Kurtis J. Barsi, Julia A. Chander, Gyanesh Malla, Rimy Department of Electrical Engineering and Computer Science South Dakota State University Brookings SD 57007 United States Landsat Project Science Office NASA Goddard Space Flight Center Greenbelt MD 20771 United States Remote Sensing Group Optical Sciences Center University of Arizona Tucson AZ 85721 United States Science Systems and Applications Inc. NASA Goddard Space Flight Center Greenbelt MD 20771 United States Science Applications International Corporation U.S. Geological Survey Center for Earth Resources Observation and Science Sioux Falls SD 57198 United States Image Processing Laboratory South Dakota State University Brookings SD 57007 United States

The Landsat-5 Thematic Mapper (TM) has been the workhorse of the Landsat system. Launched in 1984, it continues collecting data through the time frame of this paper. Thus, it provides an invaluable link to the past history of the land features of the Earth's surface, and it becomes imperative to provide an accurate radiometric calibration of the reflective bands to the user community. Previous calibration has been based on information obtained from prelaunch, the onboard calibrator, vicarious calibration attempts, and cross-calibration with Landsat-7. Currently, additional data sources are available to improve this calibration. Specifically, improvements in vicarious calibration methods and development of the use of pseudoinvariant sites for trending provide two additional independent calibration sources. The use of these additional estimates has resulted in a consistent calibration approach that ties together all of the available calibration data sources. Results from this analysis indicate a simple exponential, or a constant model may be used for all bands throughout the lifetime of Landsat-5 TM. Where previously time constants for the exponential models were approximately one year, the updated model has significantly longer time constants in bands 1-3. In contrast, bands 4, 5, and 7 are shown to be best modeled by a constant. The models proposed in this paper indicate calibration knowledge of 5% or better early in life, decreasing to nearly 2% later in life. These models have been implemented at the U.S. Geological Survey Earth Resources Observation and Science (EROS) and are the default calibration used for all Landsat TM data now distributed through EROS. © 2008 IEEE.

关键词： Landsat

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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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Chesapeake bay water quality monitoring using satellite imagery

Chesapeake bay water quality monitoring using satellite imag...

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作者： Ramakrishna, Bharath Chang, Chein-I. Trou, Bruce Henqemihle, Jerry Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 Microtel LLC 111 Centerway Greenbelt MD 20770

The Chesapeake Bay is a valued ecological, economic, recreational, cultural and scenic resource. The Bay watershed States and the District of Columbia, in conjunction with the EPA Chesapeake Bay Program, have worked and teamed together over the past 20 years to protect and restore the Bay ecosystem. A key component of this effort is water quality and habitat monitoring to assess the impact of management actions and natural, processes, and evaluate habitat parameters on living resources such as submerged aquatic vegetation (SAV), oysters, and fisheries. Using aerial and satellite remote sensing imagery has become a practical and effective means of monitoring water quality in a timely manner. Of particular interest in evaluation of water clarity are several initiative measures. Specifically, Secchi-Disk Transparency (SDT) and Chlorophyll α (Chl-α) have been widely accepted as critical indicators of water quality and their reliable estimation using satellite imagery provides a cost effective and speedy means for water quality monitoring. Work done at Water Resources Center, University of Minnesota has demonstrated the feasibility of performing regional assessment of lake water quality using LANDSAT image data. This paper investigates an approach similar to their work but uses a different type of satellite imagery, EO-1 ALI imagery where the SDT and Chl-a are also used as indicators to estimate water quality for the Chesapeake Bay and DC area (Potomac River). In doing so, three major issues are investigated, which are (1) the study site that is an open Bay area, not a self-contained lake;(2) investigation of applicability of equations that are used to specify the SDT and Chl-α to our Bay area study;(3) the use of a different type of satellite imagery for water quality monitoring. This paper develops techniques to address these three issues and presents preliminary experiments which show encouraging results. © World Scientific Publishing Company.

关键词： Water quality

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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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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Chao-Cheng Wu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA

One of commonly used criteria for finding an endmember set is to assume that for a given number of endmembers, p, a p-vertex simplex with its vertices specified by p 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.

关键词： Algorithm design and analysis Least squares methods Hyperspectral imaging Indexes Volume measurement Chaos Data mining Subspace constraints Computational efficiency Current measurement

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CHESAPEAKE BAY WATER QUALITY MONITORING USING SATELLITE imageRY

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International Journal of High Speed Electronics and Systems 2007年第4期17卷 681-688页

作者： BHARATH RAMAKRISHNA CHEIN-I CHANG BRUCE TROU JERRY HENQEMIHLE Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA Microtel LLC 111 Centerway Greenbelt MD 20770 USA

The Chesapeake Bay is a valued ecological, economic, recreational, cultural and scenic resource. The Bay watershed States and the District of Columbia, in conjunction with the EPA Chesapeake Bay Program, have worked and teamed together over the past 20 years to protect and restore the Bay ecosystem. A key component of this effort is water quality and habitat monitoring to assess the impact of management actions and natural processes, and evaluate habitat parameters on living resources such as submerged aquatic vegetation (SAV), oysters, and fisheries. Using aerial and satellite remote sensing imagery has become a practical and effective means of monitoring water quality in a timely manner. Of particular interest in evaluation of water clarity are several initiative measures. Specifically, Secchi-Disk Transparency (SDT) and Chlorophyll a (Chl- a ) have been widely accepted as critical indicators of water quality and their reliable estimation using satellite imagery provides a cost effective and speedy means for water quality monitoring. Work done at Water Resources Center, University of Minnesota has demonstrated the feasibility of performing regional assessment of lake water quality using LANDSAT image data. This paper investigates an approach similar to their work but uses a different type of satellite imagery, EO-1 ALI imagery where the SDT and Chl- a are also used as indicators to estimate water quality for the Chesapeake Bay and DC area (Potomac River). In doing so, three major issues are investigated, which are (1) the study site that is an open Bay area, not a self-contained lake; (2) investigation of applicability of equations that are used to specify the SDT and Chl- a to our Bay area study; (3) the use of a different type of satellite imagery for water quality monitoring. This paper develops techniques to address these three issues and presents preliminary experiments which show encouraging results.

关键词： Chesapeake Bay EO-1 ALI imagery Secchi Depth (SDT) Chl- a

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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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Classification Fusion in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 947-955页

作者： LIU Chun-Ting HUO Hong FANG Tao LI De-Ren SHEN Xiao Institute of Image Processing ＆ Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079

In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.

关键词： Wireless sensor networks classification fusion wavelet decomposition weighted k-nearest-neighbor Dempster-Shafer theory

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Using Linear Intersection for Node Location Computation in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 907-914页

作者： SHI Qin-Qin HUO Hong FANG Tao LI De-Ren Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei 430072

Knowing the locations of nodes in wireless sensor networks (WSN) is essential for many applications. Nodes in a WSN can have multiple capabilities and exploiting one or more of the capabilities can help to solve the localization problem. In this paper, we assume that each node in a WSN has the capability of distance measurement and present a location computation technique called linear intersection for node localization. We also propose an applied localization model using linear intersection and do some concerned experiments to estimate the location computation algorithm.

关键词： wirelell sensor network localization linear intersection error

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Unsupervised Unmixing of Hyperspectral imagery

Unsupervised Unmixing of Hyperspectral Imagery

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Yahya M. Masalmah Miguel Velez-Reyes Laboratory for Applied Remote Sensing and Image ProcessingElectrical and Computer Engineering Department University of Puerto Rico Mayaguez Puerto Rico

This paper presents an approach for simultaneous determination of end members and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF using Gauss-Seidel method. This algorithm alternates between the end members matrix updating step and the abundance estimation step until convergence is achieved. Preliminary results using a subset of the Enrique Reef image data are presented. These results show the potential of the method to solve the unsupervised unmixing problem.

关键词： Hyperspectral imaging Hyperspectral sensors Spatial resolution Pixel remote sensing Spectroscopy Laboratories image processing Gaussian processes Convergence

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An Over-Complete Independent Component Analysis (ICA) Approach to Magnetic Resonance image Analysis

An Over-Complete Independent Component Analysis (ICA) Approa...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jing Wang C.-I. Chang Hsiang Ming Chen C.C.-C. Chen Jyh Wen Chai Yen-Chieh Ouyang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan

This paper presents a new application of independent component analysis (ICA) in magnetic resonance (MR) image analysis. One of most successful applications for ICA-based approaches in MR imaging is functional MRI (fMRI) which basically deals with one-dimensional temporal signals. The ICA approach proposed in this paper is rather different and considers a set of MR images acquired by different pulse sequences as a 3-dimensional image cube and performs image analysis rather than signal analysis. One major difference between the fMRI-based ICA approaches and our proposed ICA-based image analysis is that the ICA used in the former is undercomplete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications

关键词： Independent component analysis Magnetic resonance image analysis image sequence analysis Magnetic resonance imaging Signal processing image processing Principal component analysis Pixel remote sensing

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