John R. Jensen

University of South Carolina

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Jungho Im

Ulsan National Institute of Science and Technology

Michael E. Hodgson

University of South Carolina

Elijah Ramsey

United States Geological Survey

Steven R. Schill

The Nature Conservancy

H.E. Mackey

Bidwell Memorial Presbyterian Church

Jason A. Tullis

University of Arkansas at Fayetteville

L. R. Tinney

Analysis Group (United States)

Bruce A. Davis

Johnson Space Center

Sunil Narumalani

University of Nebraska–Lincoln

Maria Garcia-Quijano

University of South Carolina

Cooperative Institutions

University of South Carolina

University of California, Santa Barbara

University of Kansas

Brigham Young University

Savannah River National Laboratory

Texas A&M University

The Ohio State University

National Oceanic and Atmospheric Administration

Center For Remote Sensing (United States)

Johnson Space Center

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Remote sensing for urban planning

Bruce A. Davis Nicholas J. Schmidt John R. Jensen Dave J. Cowen Joanne N. Halls

Utility companies are challenged to provide services to a highly dynamic customer base. With factory closures and shifts in employment becoming a routine occurrence, the utility industry must develop new techniques to maintain records and plan for expected growth. BellSouth Telecommunications, the largest of the Bell telephone companies, currently serves over 13 million residences and 2 million commercial customers. Tracking the movement of customers and scheduling the delivery of service are major tasks for BellSouth that require intensive manpower and sophisticated information management techniques. Through NASA's Commercial Remote Sensing Program Office, BellSouth is investigating the utility of remote sensing and geographic information system techniques to forecast residential development. This paper highlights the initial results of this project, which indicate a high correlation between the U.S. Bureau of Census block group statistics and statistics derived from remote sensing data.

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Remote Sensing of Mangrove Wetlands: Relating Canopy Spectra to Site-Specific Data

Photogrammetric Engineering & Remote Sensing (1996)

Elijah Ramsey John R. Jensen

Remote sensing was examined as a tool to describe the spectral and structural changes within and between mangrove species and community types. To accomplish this goal, high-resolution canopy reflectance spectra were obtained at 21 mangrove sites in southwest Florida. This was in addition to leaf spectra, canopy closure, height, and species composition from a number of these sites. High relative variability typified measurements of canopy reflectance spectra, canopy height, and percent species composition, while leaf reflectance variances within species (black or red, about 0.04 to 0.06 percent) were higher than between species (about 0.02 percent). Mean reflectances were generated for the blue, green, red, and near-infrared (NIR) wavelength regions from the obtained canopy reflectance spectra by using either user-defined bandwidths or bandwidths defined for the Advanced Very High Resolution Radiometer, Thematic Mapper, and XMS (SPOT) sensors.

Thematic Mapper

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Significance of Altitude and Posting Density on Lidar-derived Elevation Accuracy on Hazardous Waste Sites

Photogrammetric Engineering & Remote Sensing (2008)

Maria Garcia-Quijano John R. Jensen Michael E. Hodgson Brian C. Hadley J.B. Gladden

Elevation (ballistics)

10.14358/pers.74.9.1137

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Object‐based change detection using correlation image analysis and image segmentation

International Journal of Remote Sensing (2007)

Jungho Im John R. Jensen Jason A. Tullis

This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques. The correlation image analysis is based on the fact that pairs of brightness values from the same geographic area (e.g. an object) between bi‐temporal image datasets tend to be highly correlated when little change occurres, and uncorrelated when change occurs. Five different change detection methods were investigated to determine how new contextual features could improve change classification results, and if an object‐based approach could improve change classification when compared with per‐pixel analysis. The five methods examined include (1) object‐based change classification incorporating object correlation images (OCIs), (2) object‐based change classification incorporating neighbourhood correlation images (NCIs), (3) object‐based change classification without contextual features, (4) per‐pixel change classification incorporating NCIs, and (5) traditional per‐pixel change classification using only bi‐temporal image data. Two different classification algorithms (i.e. a machine‐learning decision tree and nearest‐neighbour) were also investigated. Comparison between the OCI and the NCI variables was evaluated. Object‐based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes (Kappa approximated 90%) than other change detection results (Kappa ranged from 80 to 85%).

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Object based

10.1080/01431160601075582

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Citations (404)

Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data

Photogrammetric Engineering & Remote Sensing (1995)

John R. Jensen Ken Rutchey Magaly Koch Sunil Narumalani

Recent and historical satellite remote sensor data were used to inventory aquatic macrophyte (especially cattail and sawgrass) changes within the Florida Everglades Water Conservation Arae 2A using Landsat Multispectral Scanner (MSS) data (1973, 1976, and 1982) and SPOT High Resolution Visible (HRV) multispectral data (1987 and 1991). The method required a single base year of remotely sensed data with adequate ground reference information (1991). Histological remotely sensed data were normalized to the base year's radiometric characteristics. Statistical clusters extracted from each date of imagery were found in relatively consistent regions of multispectral feature space (using red and near-infrared bandes) and labeled using a core cluster approach. Wetland classification maps of each year were analyzed using post-classification comparison change detection technique to produce maps of (1) cattail change and (2) change in the sawgrass/cattail mixture class. The amount of hectares in each wetland class was tabulated by year. The spatial distribution of the wetland was then overlaid onto a soil porewater phosphorus statistical surface obtained through in situ investigation. The cattail and cattail/sawgrass mixture classes appear to be spatially associated with distribution of relatively high concentrations of porewater phosphorus in Water Conservation Area 2A

Multispectral Scanner

Multispectral pattern recognition

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An Evaluation of Remote Sensing-derived Landscape Ecology Metrics for Reservoir Shoreline Environmental Monitoring

Photogrammetric Engineering & Remote Sensing (2005)

Mark W. Jackson John R. Jensen

The goal of this study was to determine the value of including landscape ecology patterns and structure metrics extracted from high-resolution, remotely-sensed imagery in the development of a Shoreline Environmental Impact Index (SEII). Methods of combining landscape ecology metrics to create a meaningful Shoreline Environmental Impact Index included multiple linear regression, multiple discriminant analysis, genetic neural networks, and feed-forward, backpropagation neural networks. The landscape ratings produced by the SEII’s generated using these methods were then compared to landscape ratings by experts. There was very little difference in the performance of several SEII’s generated despite differences in metrics and their weighting chosen by the different methods. The ratings from all methods showed their ability to reflect the expert ratings with moderate accuracy: � 84 percent agreement. Conclusions indicate that the contributions of landscape metrics to the ability of an SEII to discriminate between levels of shoreline degradation are variable, dependent upon the method of combination. Any of the current forms of the SEII is suitable for generating general indication of shoreline health.

10.14358/pers.71.12.1387

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An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs

Remote Sensing of Environment (2002)

Michael E. Hodgson John R. Jensen Laura Schmidt Steven R. Schill Bruce A. Davis

Elevation (ballistics)

Land Cover

10.1016/s0034-4257(02)00114-1

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Citations (245)

Opening the black box of neural networks for remote sensing image classification

International Journal of Remote Sensing (2004)

Fang Qiu John R. Jensen

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a 'black box'. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classification decisions explicitly in the form of fuzzy 'if-then' rules. However, the construction of a knowledge base, especially the fine-tuning of the fuzzy set parameters of the fuzzy rules in a fuzzy expert system, is a tedious and subjective process. This research has developed a new, improved neuro-fuzzy image classification system based on the synergism between neural networks and fuzzy expert systems. It incorporates the best of both technologies and compensates for the shortcomings of each. The learning algorithms of neural networks developed here are used to automate the derivation of fuzzy set parameters for the fuzzy 'if-then' rules in a fuzzy expert system. The rules obtained, in symbolic form, facilitate the understanding of the neural network based image classification system. In addition, the image classification accuracy obtained from the improved neuro-fuzzy system was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.

Black box

Contextual image classification

10.1080/01431160310001618798

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Creation of digital terrain models using an adaptive lidar vegetation point removal process

Photogrammetric Engineering & Remote Sensing (2002)

Karen Schuckman George T. Raber John R. Jensen Steven R. Schill

Commercial small-footprint lidar remote sensing has become an established tool for the creation of digital terrain models (DTMs). Unfortunately, even after the application of lidar vegetation point removal algorithms, vertical DTM error is not uniform and varies according to land cover. This paper presents the results of the application of an adaptive lidar vegetation removal process to a raw lidar dataset of a small area in North Carolina. This process utilized an existing lidar vegetation point removal algorithm in which the parameters were adaptively adjusted based on a vegetation map. The vegetation map was derived through the exclusive use of the lidar dataset, making the process independent of ancillary data. The vertical error and surface form of the resulting DTM were then compared to DTMs created using traditional techniques. The results indicate that the adaptive method produces a superior DTM.

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Citations (104)

Optimizing the binary discriminant function in change detection applications

Remote Sensing of Environment (2008)

Jungho Im John R. Jensen Michael E. Hodgson

Discriminant function analysis

10.1016/j.rse.2008.01.007

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Citations (38)