John Jacobson

Hyperspectral matched filter with false-alarm mitigation

One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. One algorithm that is widely used in hyperspectral detection and successfully suppresses the background in many situations is the matched filter detector. However, the matched filter also produces false alarms in many situations. We use three simple and well-established concepts-the target-background replacement model, the matched filter, and Mahalanobis distance-to develop the matched filter with false alarm mitigation (MF-FAM), a dual-threshold detector capable of eliminating many matched filter false alarms. We compare this algorithm to the mixture tuned matched filter (MTMF), a popular approach to matched filter false alarm mitigation found in the ENVI® software environment. The two algorithms are shown to produce nearly identical results using real hyperspectral data, but the MF-FAM is shown to be operationally, computationally, and theoretically simpler than the MTMF.

Statistical characterization of hyperspectral background clutter in the reflective spectral region

Hyperspectral imaging systems for daylight operation measure and analyze reflected and scattered radiation in p-spectral channels covering the reflective infrared region 0.4-2.5 microm. Consequently, the p-dimensional joint distribution of background clutter is required to design and evaluate optimum hyperspectral imaging processors. In this paper, we develop statistical models for the spectral variability of natural hyperspectral backgrounds using the class of elliptically contoured distributions. We demonstrate, using data from the NASA AVIRIS sensor, that models based on the multivariate t-elliptically contoured distribution capture with sufficient accuracy the statistical characteristics of natural hyperspectral backgrounds that are relevant to target detection applications.

Performance limitations of temperature–emissivity separation techniques in long-wave infrared hyperspectral imaging applications

Accurate estimation or retrieval of surface emissivity from long-wave infrared or thermal infrared (TIR) hyperspectral imaging data acquired by airborne or spaceborne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation and temperature–emissivity separation (TES). The most widely used TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the atmospheric transmission function. We develop a model to explain and evaluate the performance of TES algorithms using a smoothing approach. Based on this model, we identify three sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise. For each TES smoothing technique, we analyze the bias and variability of the temperature errors, which translate to emissivity errors. The performance model explains how the errors interact to generate temperature errors. Since we assume exact knowledge of the atmosphere, the presented results provide an upper bound on the performance of TES algorithms based on the smoothness assumption.

Effects of signature mismatch on hyperspectral detection algorithms

The main objective of this paper is to discuss the effects of signature mismatch on hyperspectral target detection algorithms. The main causes of mismatch are atmospheric propagation, intrinsic spectral variability, sensor noise, and sensor artifacts. We provide a theoretical analysis that shows the effects of mismatch on adaptive detection algorithms, which use estimates of background covariance matrix, and we present a systematic diagonal loading technique which provides controlled robustness to mismatch.

New insights and practical considerations in hyperspectral change detection

There are a multitude of civilian and military applications for the detection of anomalous changes in hyperspectral images. Anomalous changes occur when the material within a pixel is replaced. Environmental factors that change over time, such as illumination, will affect the radiance of all the pixels in a scene, despite the materials within remaining constant. The goal of an anomalous change detection algorithm is to suppress changes caused by the environment, and detect pixels where the materials within have changed. Anomalous change detection is a two step process. Two co-registered images of a scene are first transformed to maximize the overall correlation between the images, then an anomalous change detector (ACD) is applied to the transformed images. The transforms maximize the correlation between the two images to attenuate the environmental differences that distract from the anomalous changes of importance. Several categories of transforms with different optimization parameters are discussed and compared. One of two types of ACDs are then applied to the transformed images. The first ACD uses the difference of the two transformed images. The second concatenates the spectra of two images and uses an aggregated ACD. A comparison of the two ACD methods and their effectiveness with the different transforms is done for the first time.

Wavelength calibration correction for ground radiance spectra in LWIR hyperspectral imagery

Accurate retrieval of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data is necessary for many scientific and defense applications. Emissivity estimation consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an atmospheric estimate to convert the at-aperture radiance to ground radiance. Using the ground radiance, TES produces a temperature and emissivity estimate. TES algorithms require an accurate atmospheric model, and assumes that emissivity spectra for solids are smooth, compared to atmospheric features. A high-resolution atmospheric model is band-averaged to the sensors spectral response function (SRF). Characterization and maintenance of the SRF is difficult, and errors cause rough emissivity estimates. We propose a method where spectra with smooth reflective emissivities are used to correct errors from the SRF. In-Scene AC (ISAC) methods can be used to find accurate estimates of the band-averaged atmospheric upwelling and transmission, but not the downwelling radiance which is needed for TES. Typical TES methods use a model for the downwelling radiance and an assumed SRF, which will differ from the true SRF causing unnaturally rough emissivity estimates. While ISAC estimates include the true SRF it is difficult to separate the SRF from these measurements. Instead of estimating the SRF directly, our method uses smooth low emissivity materials to produce a correction for the downwelling radiance that matches the true band-averaged values. We demonstrate this technique using simulated data.

Measurement campaign for hyperspectral imaging in complex illumination environments

The problem of spectral reflectance retrieval of surfaces via remote hyperspectral imaging is challenging even in benign scenarios, and becomes dramatically more difficult under complex illumination conditions. Shadows, reflections from nearby structures, and atmospheric scattering can all severely impact the observed radiance from ground-level surfaces. In order to study this problem, MIT Lincoln Laboratory recently conducted an airborne data collection experiment that included hyperspectral, laser radar, and pan-chromatic modalities. A comprehensive ground truth data set and extensive efforts directed at sensor characterization makes this data set ideal for the development of hyperspectral exploitation algorithms.

Modeling the effects of atmospheric propagation for spectral libraries of natural backgrounds

The statistics of natural backgrounds extracted from an Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral datacube collected over Fort AP Hill, VA, were used to demonstrate the effects of the two atmospheric components of a statistical end-to-end performance prediction model. New capabilities in MODTRANTM5 were used to generate coefficients for linear transformations used in the atmospheric transmission and compensation components of a typical end-to-end model. Model radiance statistics, calculated using reflectance data, is found to be similar to the original AVIRIS radiance data. Moreover, if identical atmospheric conditions are applied in the atmospheric transmission and in the atmospheric compensation model components and the effects of sensor noise are disregarded, the resulting reflectance statistics are identical to the original reflectance statistics.