Joseph Meola

Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations

Hyperspectral change detection has been shown to be a promising approach for detecting subtle targets in complex backgrounds. Reported change-detection methods are typically based on linear predictors that assume a space-invariant affine transformation between image pairs. Unfortunately, several physical mechanisms can lead to a significant space variance in the spectral change associated with background clutter. This may include shadowing and other illumination variations, as well as seasonal impacts on the spectral nature of the vegetation. If not properly addressed, this can lead to poor change-detection performance. This paper explores the space-varying nature of such changes through empirical measurements and investigates spectrally segmented linear predictors to accommodate these effects. Several specific algorithms are developed and applied to change imagery captured under controlled conditions, and the impacts on clutter suppression and change detection are quantified and compared. The results indicate that such techniques can provide markedly improved performance when the environmental conditions associated with the image pairs are substantially different.

Detecting Changes in Hyperspectral Imagery Using a Model-Based Approach

Within the hyperspectral community, change detection is a continued area of interest. Interesting changes in imagery typically correspond to changes in material reflectance associated with pixels in the scene. Using a physical model describing the sensor-reaching radiance, change detection can be formulated as a statistical hypothesis test. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. The proposed physical model incorporates terms to account for both direct and diffuse shadow fractions to help mitigate false alarms associated with shadow differences between scenes. The resulting generalized likelihood ratio test (GLRT) provides an indicator of change at each pixel. The maximum likelihood estimates of the physical model parameters used for the GLRT are obtained from the entire joint data set to take advantage of coupled information existing between pixel measurements. Simulation results using synthetic and real imagery demonstrate the efficacy of the proposed approach.

Airborne hyperspectral detection of small changes.

Hyperspectral change detection offers a promising approach to detect objects and features of remotely sensed areas that are too difficult to find in single images, such as slight changes in land cover and the insertion, deletion, or movement of small objects, by exploiting subtle differences in the imagery over time. Methods for performing such change detection, however, must effectively maintain invariance to typically larger image-to-image changes in illumination and environmental conditions, as well as misregistration and viewing differences between image observations, while remaining sensitive to small differences in scene content. Previous research has established predictive algorithms to overcome such natural changes between images, and these approaches have recently been extended to deal with space-varying changes. The challenges to effective change detection, however, are often exacerbated in an airborne imaging geometry because of the limitations in control over flight conditions and geometry, and some of the recent change detection algorithms have not been demonstrated in an airborne setting. We describe the airborne implementation and relative performance of such methods. We specifically attempt to characterize the effects of spatial misregistration on change detection performance, the efficacy of class-conditional predictors in an airborne setting, and extensions to the change detection approach, including physically motivated shadow transition classifiers and matched change filtering based on in-scene atmospheric normalization.

Modeling and estimation of signal-dependent noise in hyperspectral imagery.

The majority of hyperspectral data exploitation algorithms are developed using statistical models for the data that include sensor noise. Hyperspectral data collected using charge-coupled devices or other photon detectors have sensor noise that is directly dependent on the amplitude of the signal collected. However, this signal dependence is often ignored. Additionally, the statistics of the noise can vary spatially and spectrally as a result of camera characteristics and the calibration process applied to the data. Here, we examine the expected noise characteristics of both raw and calibrated visible/near-infrared hyperspectral data and provide a method for estimating the noise statistics using calibration data or directly from the imagery if calibration data is unavailable.

The SHARE 2012 data campaign

A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.

Power scalable >25 W supercontinuum laser from 2 to 2.5 μm with near-diffraction-limited beam and low output variability

A power scalable thulium-doped fiber-amplifier-based supercontinuum (SC) laser covering the shortwave infrared region from 2 to 2.5 μm is demonstrated. The SC laser has an average power up to 25.7 W and a spectral density of >12 dBm/nm. Power scalability of the laser is proven by showing that the SC laser maintains a nearly constant spectral output, beam quality (M(2) measurements), and output spectral stability as the SC average power is scaled from 5 to 25.7 W average output power. We verify that the SC laser beam is nearly diffraction limited with an M(2)<1.2 for all power levels. Output spectral stability measurements with power scaling show a radiometric variability of <0.8% across the entire SC spectrum.

SpecTIR hyperspectral airborne Rochester experiment data collection campaign

A multi-modal (hyperspectral, LiDAR, and multi-spectral) imaging data collection campaign was conducted at the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, in the Rochester, New York, area July 26-29, 2010. The campaign was titled the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) and collected data in support of nine simultaneous unique experiments, several of which leveraged data from multiple modalities. Airborne imagery was collected over the city of Rochester with hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) sensors. Sites for data collection included the Genesee River, sections of downtown Rochester, and the RIT campus. Experiments included sub-pixel target detection, water quality monitoring, thermal vehicle tracking and wetlands health assessment. An extensive ground truthing effort was accomplished in addition to the airborne imagery collected. The ultimate goal of this comprehensive data collection campaign was to provide a community sharable resource that would support additional experiments. This paper details the experiments conducted and the corresponding data that were collected in conjunction with this campaign.

Anomaly Detection in Hyperspectral Imagery: Comparison of Methods Using Diurnal and Seasonal Data

The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background model of a hyperspectral image and measuring anomalies as image pixels that do not conform properly to that given model. In this study we compare the performance over diurnal and seasonal changes for several different anomaly detection methods found in the literature and a new anomaly detector that we refer to as the fuzzy cluster-based anomaly detector. Here we also compare the performance of several anomaly-based change detection algorithms. Our results indicate that all anomaly detectors tested in this experimentation exhibit strong performance under optimum illumination and environmental conditions. However, our results point toward a significant performance advantage for cluster-based anomaly detectors in the presence of adverse environmental conditions.

Image misregistration effects on hyperspectral change detection

This paper covers the impact of registration errors between two images on chronochrome and covariance equalization predictors used for hyperspectral change detection. Hyperspectral change detection involves the comparison of data collected of the same spatial scene on two different occasions to try to identify anomalous man-made changes. Typical change detection techniques employ a linear prediction method followed by a subtraction step to identify changes. These linear predictors rely upon statistics from both scenes to determine a respective gain and offset. Chronochrome and covariance equalization remain two common predictors used in the change detection process. Chronochrome relies upon a cross-covariance matrix for prediction whereas covariance equalization relies solely upon the individual covariance matrices. In theory, chronochrome seems more susceptible to image misregistration issues as joint statistic estimates may suffer with registration error present. This paper examines the validity of this assumption. Using a push-broom style imaging spectrometer mounted on a pan and tilt, visible to near infrared data of scenes suitable for change detection analysis are gathered. The pan and tilt system ensures initial misregistration of the data is minimal. Using simple translations of the scenes, misregistration impacts upon prediction error and change detection are examined for varying degrees of shift.

Field trial of active remote sensing using a high-power short-wave infrared supercontinuum laser

Field trial results of a 5 W all-fiber broadband supercontinuum (SC) laser covering the short-wave infrared (SWIR) wavelength bands from ~1.55 to 2.35 μm are presented. The SC laser is kept on a 12 story tower at the Wright Patterson Air Force Base and propagated through the atmosphere to a target 1.6 km away. Beam quality of the SC laser after propagating through 1.6 km is studied using a SWIR camera and show a near diffraction limited beam with an M(2) value of <1.3. The SC laser is used as the illumination source to perform spectral reflectance measurements of various samples at 1.6 km, and the results are seen to be in good agreement with in-lab measurements using a conventional lamp source. Spectral stability measurements are performed after atmospheric propagation through 1.6 km and show a relative variability of ~4%-8% across the spectrum depending on the atmospheric turbulence effects. Spectral stability measurements are also performed in-lab and show a relative variability of <0.6% across the spectrum.