Michael K. Griffin

NLOS UV communication for distributed sensor systems

Atmospheric scattering of ultraviolet light is examined as a mechanism for short-range, non-line-of-sight (NLOS) communication between nodes in energy-constrained distributed sensor networks. The physics of scattering is discussed and modeled, and progress in the development of solid state sources and detectors is briefly summarized. The performance of a representative NLOS UV communication system is analyzed by means of a simulation model and compared to conventional RF systems in terms of covertness and transceiver power. A test bed for evaluating NLOS UV communication hardware and modulation schemes is described.

Cloud cover detection algorithm for EO-1 Hyperion imagery

A cloud cover detection algorithm was developed for application to EO-1 Hyperion hyperspectral data. The algorithm uses only bands in the reflected solar spectral regions to discriminate clouds from surface features and was designed to be used on-board the EO-1 satellite as part of the EO-1 Extended Mission Phase of the EO-1 Science Program. The cloud cover algorithm uses only 6 bands to discriminate clouds from other bright surface features such as snow, ice, and desert sand. The code was developed using 20 Hyperion scenes with varying cloud amount, cloud type, underlying surface characteristics and seasonal conditions. Results from the application of the algorithm to these test scenes is given with a discussion on the accuracy of the procedure used in the cloud cover discrimination. Compared to subjective estimates of the scene cloud cover, the algorithm was typically within a few percent of the estimated total cloud cover.

Characterization and delineation of plumes, clouds, and fires in hyperspectral images

Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.

Modeling of LWIR Hyperspectral System Performance for Surface Object and Effluent Detection Applications

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to the longwave infrared (LWIR). The model uses statistical descriptions of surface emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This paper presents the theory and operation of extensions made to the model to cover the LWIR. Theory is presented on combining both surface spectral emissivity variation with surface temperature variation on the upwelling radiance measured by a downward-looking LWIR hyperspectral sensor. Comparisons of the model predictions with measurements from an airborne LWIR hyperspectral sensor at the DoE ARM site are presented. Also discussed is the implementation of a plume model and radiative transfer equations used to incorporate a thin man-made effluent plume in the upwelling radiance. Example parameter trades are included to show the utility of the model for sensor design and operation applications.

A procedure for embedding effluent plumes into LWIR imagery

Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.

Characterization and delineation of plumes, clouds and fires in hyperspectral images

Techniques for scene characterization can utilize individual or combinations of visible and IR spectral bands to identify specific features in an image. This paper deals primarily with the problem of characterization of a partially smoke- or cloud-filled atmosphere. Proper analysis of the scene allows further sensing of underlying surface features such as actively burning and burn scarred regions. Both a physics-based and a semi-automated feature extraction approach are used for identifying and characterizing features in a set of AVIRIS scenes dominated by areas of smoke plumes, clouds and burning grassland as well as burnt vegetation. A combination of the two approaches is used to both discriminate and classify various features in a smoke/cloud filled scene. An AVIRIS scene chosen for initial testing of the two algorithms was collected on 20 August 1992 in the foothills east of Linden, California. A typical AVIRIS scene covers a 10 km/spl times/10 km area at 20 m pixel resolution and 224 contiguous spectral bands of data over the range 400 to 2500 nm. The scene consists of a grass fire producing a thick plume of smoke extending toward the east. A cloud produced by the thermal properties of the fire overlies the smoke plume. Northwest of the main fire, two smoldering fires produce a thin veil of smoke that covers much of the upper half of the scene. The southwest portion of the scene is cloud and smoke free. This scene provides a variety of atmospheric and surface features from which to orient and characterize. A plot of the apparent reflectance of various identified features was made. The cloud is significantly brighter than the smoke over the entire spectral region.

Understanding radiative transfer in the midwave infrared: a precursor to full-spectrum atmospheric compensation

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered.

Examples of EO-1 data analysis

To demonstrate the utility of EO-1 data, combined analysis of panchromatic, multispectral (ALI, Advanced Land Imager) and hyperspectral (Hyperion) data was conducted. In particular, the value added by HSI with additional spectral information will be illustrated. Data sets from Coleambally Irrigation Area, Australia on 7 March 2000 and San Francisco Bay area on 17 January 2000 are employed for the analysis. Analysis examples are shown for surface characterization, anomaly detection, spectral unmixing and image sharpening.

Radiative transfer in the midwave infrared applicable to full spectrum atmospheric characterization

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered

EO-1 hyperion data analysis applicable to cloud detection, coastal characterization and terrain classification

The EO-1 satellite is part of NASAs New Millennium Program (NMP). It consists of three imaging sensors: the multispectral Advanced Land Imager (ALI), Hyperion and Atmospheric Corrector. Hyperion provides a high-resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 micron) with a 30 m resolution. Three examples of EO-1 Hyperion data analysis efforts are illustrated. The discussion begins with a cloud cover algorithm that utilizes only solar reflective channels to discriminate cloud types and cloud/surface features. The algorithm is applied to a variety of Hyperion scenes that depict various cloud types, surface features and seasonal conditions. The second example illustrates hyperspectral application to coastal characterization. Hyperion data from Chesapeake Bay from 19 February 2002 are analyzed. Chlorophyll retrieval results are shown. The results compare favorably with data from other sources. The third example deals with terrain analysis for background classification applications. Abundance levels of lush vegetation and bare soil are estimated for image pixels located between different fields of crops over Coleambally Irrigation Area, Australia on 7 March 2000.