Laboratory spectral measurements were conducted to evaluate the potential of hyperspectral technologies, both reflective and emissive, to detect Polychlorinated Biphenyl (PCB) in contaminated soils. Soil sample standards of silt, clay, sand, and mixed textures were contaminated with Polychlorinated Biphenyl (PCB) oil with concentrations varying between 0% and 10% (100 000 ppm) of PCB. An ASD (0.35 to 2.5 microns) and a FSR (1.6 to 12 microns) spectrometers were used to measure the reflectance of pure and contaminated soil. Two spectral regions, one in the SWIR and one in the MWIR were significantly correlated to the soil PCB concentrations. It was shown that 5W30 engine oil and PCB absorb similarly in the two spectral regions. Sand and clay soils did not respond the same way as the other soil type. The MWIR CH absorption band between 3330 and 3630 was found the most promising to detect PCB contaminated soils using hyperspectral remote sensing,

The Canadian Space Agency (CSA) in collaboration with the Naval Research Laboratory (NRL) and NASA are considering a coastal and inland water color hyperspectral imager as a complement to the NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. This hyperspectral imager with 100 m spatial sampling would be specifically designed to sample the coastal oceans, estuaries and lakes, and will help improve the monitoring and better understand quality and productivity of coastal and inland waters and their impact on the coastal communities' well-being and economic activities. This paper describes the user community needs for spaceborne fine-spatial-resolution coastal and inland water hyperspectral imaging, the specifications of the hyperspectral payload that addresses the users' needs, payload concept design and related technical challenges.

Presents information on the Gender Underrepresentation Program.

This paper investigated the potential of using LWIR spectral emissivity signatures to detect unexploded ordnance in the impact ranges of the Canadian Forces Bases. The experimental setup was composed of inert projectiles of various sizes and coating, and various potential false alarm objects. LWIR Hypercam images were acquired at 30 minutes intervals between 9:30 on Aug 23 and 21h00 on Aug 24 2013 from a height of 20m at nadir. Images were processed to emissivity and the Generalized Likelihood Ratio Test (GLRT) was used to perform the detection. Results show that the GLRT is suitable for detecting the paint used to cover the projectiles if they are not covered by vegetation. Other detected targets, such as glass and wood, are spectrally distinct and would not appear as false alarms.

The papers in this special issue were presented at the 35th IGARSS 2014 Canadian Symposium on Remote Sensing event, held from July 13 to 18 in Quebec City, Canada.

The processing chain leading to specific material detection in hyperspectral imagery implies the use of atmospherically corrected images of emissivity or reflectance before comparing image signatures to a database of materials' signatures. This is a sensible approach for the reflective hyperspectral bands l and when the pixels are completely filled with a uniform material in the LWIR bands (8 to 12 microns). In the LWIR, the atmospheric correction process is different of what is used in the reflective bands and involves the use of a temperature and emissivity separation process (TES). If the pixel is not filled with a uniform material and the measured radiance is produced from the mix of materials having different emissivity and temperatures, the output of the TES will not be linear in temperature and in emissivity and will be contaminated by the non-linear mix of the temperature and emissivity of the materials leading to a potential for confusion during the detection process. In this paper, we propose a detection approach using the ground leaving radiance that is used directly to perform detection using emissivity signatures contained in a database. The detection results using this process are compared with the detection results using the output of a TES algorithm. The study is performed in simulation without noise and with the exact knowledge of the downwelling irradiance. The results show that a detection algorithm using the ground leaving radiance performs better than its counterpart using the emissivity when the difference in temperature increases.

Imaging Fourier-transform infrared (FTIR) spectroscopy is a powerful method for the passive remote detection and identification of vapor emanations and surface contaminations. In the Defense and Security context, imaging FTIR can be used for the remote surveillance of locations suspected of illicit product fabrications. DRDC Valcartier recently initiated the development and field-validation of the novel imaging FTIR sensor MoDDIFS (Multi-option Differential Detection and Imaging Fourier Spectrometer) to address this remote sensing application. The proposed system combines the clutter suppression efficiency of the differential detection approach with the high spatial resolution provided by the hyperspectral imaging approach. The MoDDIFS sensor includes two configuration options, one for remote gas detection, and the other for polarization sensing of surface contamination. This paper reviews recent results obtained with MoDDIFS for the passive standoff detection of gases and liquid contaminants. Hyperspectral measurements done on difluoroethane, diethyl ether (gases) and SF96 (liquid) serve to develop, test and validate GLRT-type detection algorithms. Detection results are presented and discussed in terms of the GLRT detection attributes.

This research examined the spectral response of poplar (Populus deltoides, Populus trichocarpa), wheat (Triticum aestivum), and canola (Brassica napus) leaves subjected to fumigation with gaseous phase toxic industrial chemical gases (TICs). The gases include ammonia (NH3), sulphur dioxide (SO2), hydrogen sulphide (H2S), chlorine (Cl2), and hydrogen cyanide (HCN). This study aimed to determine if: (1) vegetation subjected to TICs could be distinguished from background vegetation during varying growth stages and environmental stresses; and, (2) different TICs could be distinguished based on the spectral response of vegetation. The results showed that both environmental and TICs induced similar spectral features inherent to plants, which are related primarily to chlorophyll and water loss. These features include pigments in the visible and cellulose, lignin, lipids starches, and sugars in the SWIR. Although no specific spectral features could be tied to individual TICs an analysis of the data using vegetation indices showed that the TICs and environmental stresses result in diagnostic trends from healthy mature to highly stressed leaves. In addition combinations of specific indices could be used to distinguish the effects of NH3, SO2, Cl2 and their effect from that of other treatments of the study. The continued goal for this research program is to develop a remote detection capability for hazardous events such as a toxic gas leak. Our findings at the leaf level suggest that damage can be detected within 48 hrs and should last for an extended period. Thus, the next experimental step is to test if the results shown here at the leaf level can also be detected with airborne and satellites systems.

This article presents an evaluation of a previously proposed noise reduction technique for hyperspectral imagery with regard to its use in remote sensing applications. Target detection from hyperspectral imagery was selected as an example for the evaluation. A hyperspectral datacube acquired using the airborne Shortwave Infrared Full Spectrum Imager (SFSI)-II with man-made targets deployed in the scene of the datacube was tested. In addition to an evaluation using the receiver operating characteristic (ROC) curve approach, we used a spectral unmixing technique to generate the fraction images of the target materials, measured the area of the targets derived from the datacube before and after applying the noise reduction technology, and then compared the derived target areas to the real targets to assess the detectability of the targets. The area ratio between a derived target and the real target was used as the criterion in the evaluation. The evaluation results show that the noise reduction technique can help to better serve remote sensing applications. The small targets that cannot be detected from the original datacube were detected after the noise reduction using the technology.

Recently, DRDC Valcartier has been investigating novel ground-based longwave hyperspectral imaging (HSI) remote sensing techniques. Specific projects include the development of a new ground-based sensor called MoDDIFS (Multi-Option Differential Detection and Imaging Fourier Spectrometer), which is a leading edge infrared (IR) hyperspectral imaging (HSI) sensor optimized for the standoff detection of explosive vapours and precursors. The development of the MoDDIFS sensor is based on the integration of two innovative technologies: (1) the differential Fourier-transform infrared (FTIR) radiometry technology found in the Compact Atmospheric Sounding Interferometer (CATSI) previously developed by DRDC Valcartier, and (2) the HSI technology developed by Telops. The MoDDIFS sensor will offer the optical subtraction capability of the CATSI system but at high-spatial resolution using an MCT focal plane array of 84×84 pixels. The MoDDIFS sensor offers the potential of simultaneously measuring differential linear polarizations to further reduce the clutter in the measured radiance.

Acid mine drainage resulting from mine tailings poses an environmental threat. An important initial step towards the reclamation of mine tailing sites is to detect the presence of acid-generating, sulphide-rich minerals and determine their spatial distribution. In this study, the potential of hyperspectral remote sensing for characterizing mine tailings is investigated. The study site is located in northern Ontario, Canada, and the data were collected with PROBE-1, an imaging spectrometer that covers the visible, near-infrared, and shortwave-infrared spectral ranges. The results indicate that using the weakly constrained linear spectral unmixing technique PROBE-1 data can provide information on mineral compositions of the tailing surface. The spatial locations and associations of acid-generating source minerals such as pyrite and pyrrhotite along with their oxidation products (e. g., copiapite, jarosite, ferrihydrite, goethite, and hematite) can provide information about the distribution of oxidation processes at the site. This remote mapping technique can be very valuable when attempting to identify abandoned mine-waste sites and the potential risk they might present where there are no a priori knowledge and field samples available.

We assess the effectiveness of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it can better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery using a spectral unmixing approach is selected as an example in the assess- ment. A hyperspectral datacube acquired using an airborne short-wave- infrared Full Spectrum Image II with man-made targets in the scene of the datacube is tested. Three criteria are proposed and used to evaluate the detectability of the targets derived from the datacube before and after noise reduction. The evaluation results show that the detectability of the targets is significantly improved after noise reduction using the technol- ogy. The targets not detected from the original datacube are detected with high confidence after noise reduction using the technology. A noise reduction technique that is based on a smoothing approach is also evaluated for the sake of comparison to the proposed noise reduction technology. It also improves the detectability of the targets, but is less effective than the proposed noise reduction technology. © 2009 Government agery up to 98% for the test data sets. 2 It is essential to evaluate the effectiveness of the HSSNR noise reduction technology using remote sensing algo- rithms and applications. Othman and Qian have carried out an evaluation of the noise reduction technology using inter- mediate remote sensing products. 3 They adopted two ap- proaches to evaluating the hyperspectral datasets that are denoised using the technology. The first approach evaluated the effectiveness of the noise reduction technology using narrowband vegetation indices and red-edge positions of the hyperspectral datasets, while the second approach evaluated the effectiveness using a number of spectral simi- larity measures. Evaluation results show that the HSSNR noise reduction technology yielded comparable results to existing denoising technologies for vegetation indices and red-edge positions, and superior results for spectral similar- ity measures. The detailed evaluation results of the interme- diate remote sensing products have been reported by Othman and Qian. 3 A simplified application-based evaluation was conducted on a data set for target detection applications. 4 A spectral angle mapper SAM and end members of different target materials were used. The end-member spectrum of a target material was used as the seed spectrum to match the spectra of the pixels of a target of that material to measure the derived area of the target for assessing the detectability of the target before and after applying the noise reduction technology. The experimental results show that small tar- gets, which cannot be detected in the original dataset due to inadequate SNR and low spatial resolution, are more likely to be detected after the noise of the dataset is reduced. This work attempts to assess the effectiveness of the HSSNR noise reduction technology using a target detection application for the purpose of examining whether the noise-

This paper investigates the use of hyperspectral remote sensing imagery in the 400–2500 nm wavelength range for the extraction of information suitable for monitoring mine tailings revegetation. The objectives were twofold: (i) demonstrate the usefulness of fractional texture for monitoring mine tailings revegetation using visible and near-infrared (VNIR) hyperspectral data, and (ii) investigate the benefit of adding the short-wave infrared (SWIR) bands. Compact Airborne Spectrographic Imager (casi) data were acquired over the Copper Cliff mine tailings impoundment area in the VNIR bands during the summers of 1996 and 1998. In addition, Probe-1 data were collected in the VNIR-SWIR region during the summer of 1999. Surface reflectance was retrieved from the three datasets, and spectra of the 1996 and 1998 casi datasets were resampled to match the 1999 Probe-1 spectral sampling characteristics, which resulted in 30 bands covering the 450–890 nm range. The three datasets were concatenated into one file, and 30 endmember spectra were automatically selected. Constrained linear spectral unmixing was performed using the 30 endmembers, which were then grouped into the six endmember categories, namely water, lime, fresh and oxidized tailings, and low and high photosynthetic vegetation. Image fractions were then normalized and image texture was extracted from the total vegetation fraction. Total vegetation fraction (high/low photosynthetic), total tailings fraction (fresh/oxidized), and texture of the vegetation fraction were used in a K-mean unsupervised classification, which produced the best results using seven classes (78.13% overall accuracy, Kappa coefficient of 0.74). Classification results were validated using a set of 34 ground estimates of vegetation cover and tailings. The full set of 128 bands of the 1999 Probe-1 dataset was used to investigate the contribution of the SWIR bands to monitoring the reclamation of mine tailings. A new vegetation endmember was identified as plant litter, which in many cases replaces areas labelled as low photosynthetic vegetation when using the VNIR bands only. Oxidized tailings could be separated into jarosite and goethite endmembers and lime into agricultural lime (CaMgCO3) and calcium oxide (CaO), also known as quicklime.

The development of hyperspectral remote sensing provides an opportunity to considerably improve the accuracy of vegetation species classification, because the fine spectral resolution of hyperspectral remote sensing data offers the potential to discriminate between species with subtle spectral signature differences. The high spectral resolution, on the other hand, makes the hyperspectral data highly redundant and poses great challenges in developing methodologies suited for vegetation identification. Without proper approaches to dealing with the high redundancy of hyperspectral data, important characteristics of a vegetation cover may be lost or submerged by other insignificant information which co-exists in the hyperspectral data, and thus classification accuracy may be even deteriorated. It is well recognized that using a smaller number of spectral bands can probably produce better classification accuracies than the use of all bands. As a result, feature selection including band selection is very important for vegetation cover classification using hyperspectral data as well. The commonly used approaches to represent the data by a few basic components include principal component analysis (PCA) [1] and its variants, and the minimum noise fraction (MNF) transformation [2]. The basic assumption for these methods is that remote sensing signal is stationary and ergodic across the whole image, which is not always realistic [3]. In this study, an advanced method was proposed to extract spectral features progressively from individual reflectance spectrum.

A good similarity measure is very important to ensure accurate classification of vegetation species using hyperspectral remote sensing data. In this study, the effectiveness of the existing similarity measures, spectral angle mapper (SAM), Euclidean distance (ED), and spectral information divergence (SID) was evaluated using data measured by a field spectrometer. To overcome the limitations of the existing measures, a new metric was developed based on the concept of conditional entropy.