ASPRS

PE&RS April 2003

VOLUME 69, NUMBER 4
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
JOURNAL OF THE AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING

Peer-Reviewed Articles

341 Synergistic Fusion of GPS and Photogrammetrically Generated Elevation Models
Jon P. Mills, Simon J. Buckley, and Harvey L. Mitchell

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Digital elevation models (DEMs) produced from photogrammetric data sources have long relied on the use of ground control points to give them scale and orientation. However, in areas such as coastlines, landslides, or glaciers, where identification of suitable natural features and pre-marking is difficult, the use of conventional ground control may be unfeasible. This paper reports on research that uses independently collected DEMs derived from kinematic GPS to orient surfaces produced by aerial photogrammetric methods, using a least-squares surface matching algorithm. During algorithm development, three stages of testing were carried out, using increasingly more complex datasets. Initially, simulated surfaces were used to validate the matching theory and program. Then, a DEM derived from conventional aerial photography was matched with a GPS model, highlighting the effectiveness of surface matching to recover systematic errors in datasets. Finally, surfaces derived from small format digital imagery were successfully fused with wireframe GPS surfaces, the high redundancy and automation potential creating an elegant and cheaper alternative to photocontrol.

351 Remote Sensing Texture Analysis Using Multi-Parameter and Multi-Scale Features
Yan Li and Jia-Xiong Peng

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A new multi-parameter and multi-scale (MPMS) feature set derived from fractional Brownian motion (fBm) is presented in this paper. Generally, natural textures can be modeled as fBm processes. Because fBm is characterized by a single Hurst parameter, it is not flexible enough to be used as the texture feature in classification. Therefore, an extended model is proposed, where the Hurst parameter H is no longer constant for all the scales. The shape and location of the estimation curve, when presented graphically, are fixed for a particular texture. We use the slopes of sections of the estimation curve to estimate the multi-scale Hurst parameters. Meanwhile, the unit displacement incremental power (UDIP) o² is also taken as the parameter of the process. It determines the vertical placement of the curve in the graph. Experiments prove that the MPMS feature set has advantages over some other features in the classification of SPOT images.

357 Comparing Texture Analysis Methods through Classification
Philippe Maillard

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The development and testing of two techniques of texture analysis based on different mathematical tools-the semi-variogram and the Fourier spectra are presented. These are also compared against a benchmark approach: the Gray-Level Co-occurrence Matrix. The three methods and their implementation are briefly described. Three series of experiments have been prepared to test the performance of these methods in various classification contexts. These contexts are simulated by varying the number, type and visual likeness of the texture patches used in classification tests. More specifically, their ability to correctly classify, separate, and associate texture patches is assessed. Results suggest that the classification context has an important impact on performance rates of all methods. The variogram-based and the Gray-Tone Dependency Matrix methods were generally superior, each one in particular contexts.

369 Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis
Jin Chen, Peng Gong, Chunyang He, Ruiliang Pu, and Peijun Shi

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Change-vector analysis (CVA) is a valuable technique for land-use/land-cover change detection. However, how to reasonably determine thresholds of change magnitude and change direction is a bottleneck to its proper application. In this paper, a new method is proposed to improve CVA. The method (the improved CVA) consists of two stages, Double-Window Flexible Pace Search (DFPS), which aims at determining the threshold of change magnitude, and direction cosines of change vectors for determining change direction (category) that combines single-date image classification with a minimum-distance categorizing technique. When the improved CVA was applied to the detection of the land-use/land-cover changes in the Haidian District, Beijing, China, Kappa coefficients of "change/ no-change" detection and "from-to" types of change detection were 0.87 and greater than 0.7, respectively, for all kinds of land-use changes. The experimental results indicate that the improved CVA has good potential in land-use/land-cover change detection.

381 Irrigated Vegetation Assessment for Urban Environments
Douglas Stow, Lloyd Coulter, John Kaiser, Allen Hope, Dawn Service, Katarina Schutte, and Alan Walters

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Assuring the availability of water in the southwestern United States is a major resource management problem. Irrigation of landscape vegetation within urban environments represents a large portion of the total urban water consumption for this region. Current estimates suggest that up to 50 percent of residential water is used for landscape irrigation. This paper examines the utility of Ikonos multispectral satellite imagery and expert classifier approaches for quantifying the amount and distribution of urban irrigated landscape vegetation. A decision tree, expert classifier model applied to Ikonos image and land-use GIS layer inputs was tested against conventional image classification approaches. With all branches of the decision tree activated, percentage estimates of urban irrigated vegetation versus impervious cover differed from airborne image-derived reference data by less than 8 percent. Highest agreement was achieved using all model branches except a spatial structure rule, which utilized a texture metric derived from Ikonos 1-m panchromatic data. For this same product, proportion estimates of two growth form types (Tree/Shrub and Grass) and impervious cover differed from reference data by less than 3 percent, and root-mean-square error (RMSE) values for all neighborhood-size sampling units were within 5 percent for all cover types. This "optimal" expert classifier product yielded areal proportion estimates and RMSE values that were approximately 2 percent closer to those of the reference data, compared to standard unsupervised classification applied to Ikonos multispectral data.

391 Demonstrating UAV-Acquired Real-Time Thermal Data over Fires
Vincent G. Ambrosia, Steven S. Wegener, Donald V. Sullivan, Sally W. Buechel, Stephen E. Dunagan, James A. Brass, Jay Stoneburner

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Project FiRE (First Response Experiment), a disaster management technology demonstration, was performed in 2001. The experiment demonstrated the use of a thermal multispectral scanning imager, integrated on an unmanned aerial vehicle (UAV), a satellite uplink/downlink image data telemetry system, and near-real-time geo-rectification of the resultant imagery for data distribution via the Internet to disaster managers. The FiRE demonstration provided geo-corrected image data over a controlled burn to a fire management community in near-real-time by means of the melding of new technologies. The use of the UAV demonstrated remotely piloted flight (thereby reducing the potential for loss of human life during hazardous missions), and the ability to "linger and stare" over the fire for extended periods of time (beyond the capabilities of human-pilot endurance). Improvements in a high-temperature calibrated thermal imaging scanner allowed "remote" operations from a UAV and provided real-time accurate fire information collection over a controlled burn. Improved bit-rate capacity telemetry capabilities increased the amount, structure, and information content of the image data relayed to the ground. The integration of precision navigation instrumentation allowed improved accuracies in geo-rectification of the resultant imagery, easing data ingestion and overlay in a GIS framework. We present a discussion of the feasibility of utilizing new platforms, improved sensor configurations, improved telemetry, and new geo-correction software to facilitate wildfire management and mitigation strategies.

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