Image spectroscopy was used to evaluate iron oxide acid mine drainage contamination at two U.S. Environmental Protection Agency Super Fund sites located in Colorado and New Mexico. The AVIRIS hyper-spectral remote sensing system developed by the Jet Propulsion Laboratory was used to collect the imagery data used in the analysis. The paper presents an overview of mining methods used in the area of the study, the environmental risks of acid mine drainage and the AVIRIS hyper-spectral sensing system. The two sites evaluated are located in Leadville, Colorado and the Ray Mine site in New Mexico. Imagery spectroscopy was evaluated at these two sites for identifying potential mineral pollutants and mapping their location for cleanup planning and monitoring applications. Results indicate the technology can be a very useful tool for this type of application and location.

Some types of clay, esp. montmorillonite, become slippery when getting wet. Clay movement is very harmful for various constructions and can also cause trouble for both wheeled and tracked vehicles in military operations at some rural areas when raining. We present a summary of a project using hyperspectral imaging in assisting earth roads construction planning and cross-country trafficability analysis. Spectral signature libraries are used to help identify materials and define those areas to be avoided, which have significant montmorillonite content. We perform a case study in this kind of application; some methods of data processing and analyzing are discussed. We also discussed the problems we met in this application. Hyperspectral sensing is a relatively new but mature technology; development of applications and corresponding analyzing procedures will be the major impetus of this technology.

We previously developed an algorithm for remote sensing of ocean color from space that allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past year we extended our radiative transfer calculations, allowing us to generate tables for several airborne altitudes. We also modified our lookup-table software to interpolate to sensor altitudes between those specified in the new tables. Here, we present results of atmospheric corrections using the new tables and software on hyperspectral imagery collected with NRL's recent PHILLS instrument and past AVIRIS flights.

In this paper, the maximum-value composite of images procedure from Normalized Difference Vegetation Index is used to get a cloud free image mosaic. The image mosaic is used to identify vegetation targets such as tropical forest, savanna and caatinga as well to make the vegetation cover mapping of Minas Gerais state, Brazil.

Different research groups have recently studied the concept of wavelet image fusion between panchromatic and multispectral images using different approaches. In this paper, a new approach using the wavelet based method for data fusion between hyperspectral and multispectral images is presented. Using this wavelet concept of hyperspectral and multispectral data fusion, we performed image fusion between two spectral levels of a hyperspectral image and one band of multispectral image. The reconstructed image has a root mean square error of 2.8 per pixel and a signal-to- noise ratio of 36 dB. We achieved our goal of creating a composite image that has the same spectral resolution as the hyperspectral image and the same spatial resolution as the multispectral image with minimum artifacts.

Anomaly detection presented in this paper does not need any kind of target information. In other words, target information plays no role in anomaly detection. The purpose of our anomaly detection is to locate and search for targets which are generally unknown, but relatively small with low probabilities in an image scene. These anomalous targets cannot be identified by prior knowledge. Two approaches are considered in this paper, the RX algorithm developed by Reed and Yu and a uniform target detector (UTD) derived from the low probability detection in Harsanyi's dissertation, both of which operate a matched filter form with different matched signals used in the individual approaches. The matched signal used in the RX algorithm is the pixel vector r while the UTD using the unity vector 1 the matched signal. In addition, they both can be implemented in real-time.

Feature-based matching is essential for attaining sub-pixel registration of remotely sensed imagery. In this work, we focus on two different similarity metrics which are used to match extracted features, correlation and mutual information. Although mutual information has been successfully applied to medical image registration, these metrics have not been systematically studied for remote sensing applications. This paper presents some first results in the comparison of correlation and mutual information, relative to their respective accuracy and response to noise. The study is performed using Landsat-TM data.

The emergence of a new generation of satellites, increased dependence on computer-aided cartography, and conversion of paper-based maps along with the universal acceptance of the World Wide Web as a distribution medium, has resulted in widespread availability of geospatial data. Geospatial information systems have the potential to use this wealth of data to provide high-level decision support in important military, agricultural, urban planning, transportation and environmental monitoring applications. There are many challenges to take full advantage of this geo-spatial data collection. The first step in integration is to determine the correspondence between features in different sources. This problem, called like-feature detection is addressed in this paper. In addition to using the individual attributes of features, we use the geographic context abstracted as proximity graphs, to improve the matching process. The proximity graph models the surroundings of a feature in a source and provides a measure of similarity between features in two sources. Pair-wise similarity between features of two sources is then extended to multiple sources in a graph- theoretic framework. Experiments conducted to demonstrate the viability of our approach using a variety of data sources including satellite imagery, maps, and gazetteers show that the approach is effective.

The need for fast hyperspectral data processing methods is discussed. Discussion includes the necessity of faster processing techniques in order to realize emerging markets for hyperspectral data. Several standard hyperspectral image processing methods are presented, including maximum likelihood classification, principal components analysis, and canonical analysis. Modifications of those methods are presented that are computationally more efficient than standard techniques. Recent technological developments enabling hardware acceleration of hyperspectral data processing methods are also presented as well as their applicability to various hyperspectral data processing algorithms.

Hyperspectral imaging is a relatively new technology which draws much attention from the scientists now. With information provided on hundreds of narrow and continuous bands, it finds applications in many areas such as mineral identification, environmental monitoring, agricultural survey, medical examination. However, it is not an easy task to utilize the hyperspectral images, due to the large data volume. Certain tools are needed to analyze the hyperspectral image. As an example, the ENvironment for Visualizing Images (ENVI) software is used for the study of a sample hyperspectral image.

Natural disasters have a major impact, globally and within the United States causing injury and loss of life, as well as economic losses. To better address disaster response needs, a task force has been established to leverage technological capabilities to improve disaster response management. Web based geospatial analysis is one of these important capabilities. Samples of geospatial technologies applicable to disaster management are presented. These include 3D visualization, hyperspectral imagery, LIDAR, use of spectral libraries, digital multispectral video, radar imaging systems, photogeologic analysis and geographic information systems. An example scenario of a hurricane with landfall at Mobile, Alabama is used to demonstrate the interoperable use of web-based geospatial information to support decision support systems and assist public information communication.