SWIR Imagery Research Articles

Geological investigations with high spatial resolution WV-3 satellite imagery and regional geophysics at the Haib Cu porphyry, Namibia

SummaryRecent improvements in available multi-spectral satelliteborne shortwave infrared sensors and their spatial resolution opens up the opportunity for furthering surface mineralogy mapping. Their image interpretation can be augmented with regional geophysics, e.g. subsurface structural information via magnetics or fine tuning the interpretation of mineral chemistry using radioelement data. The new higher spatial resolution satellite WorldView-3 sensor is compared in this study with the ASTER satellite imagery over the Haib copper prospect in southern Namibia using published geology and airborne hyperspectral imagery control.The results show an improvement using higher spatial resolution combined with improvements in SWIR imagery for the mapping of different AlOH clays, potentially related phyllic and argillic alteration that may be associated with structurally controlled alteration and mineralisation.

Prospectivity mapping for high sulfidation epithermal porphyry deposits using an integrated compositional and topographic remote sensing dataset

The targeting and discovery of epithermal porphyry mineral deposits can be enhanced using a structured quantitative methodology to analyse the distribution of ore deposits and model their spatial association with exploration evidence providing improved understanding on the controls of ore deposition. A novel exploration tool integrating field and ASTER SWIR and TIR satellite imagery has been developed which provides an enhanced means of resolving surface expressions of the residual silica core of the lithocap. The alteration zones were clearly resolved by the remote sensing data and an intimate spatial relationship between high-grade altered rocks and topographic highs was identified at a number of locations. A Mineral Prospectivity Modelling (MPM) approach, parameterized by the results of the remote sensing study, using a GIS-based weighted linear combination implementation of a Multi-Criteria Evaluation approach and utilising a fuzzy Analytical Hierarchy Process to elucidate expert knowledge has been implemented to target high sulfidation epithermal porphyry deposits on the Island of Lesvos, Greece. The results from this integrated altitudinal-compositional modelling approach closely matched the hydrothermal alteration mapped in the field supporting the accuracy of this MPM approach.

Shortwave infrared (1.0–2.5 μm) hyperspectral imaging of the Athabasca West Grand Rapids Formation oil sands

Previous work on the reflectance spectroscopy of oil sands has focused exclusively on the McMurray Formation of the Athabasca deposit. However, as industrial development expands into other formations, spectral research on Alberta’s other bitumen reservoirs is warranted. This study presents the first investigation of shortwave infrared (SWIR; 1.0–2.5 μm) hyperspectral imaging of Grand Rapids Formation oil sands. Comparing the spectral properties of Grand Rapids oil sands to that of the McMurray Formation reveals some key differences. Grand Rapids oil sands have lower overall reflectance and deeper 1.4- and 1.9-μm absorption features than McMurray oil sands of the same ore grade. At ore grades greater than 10 wt. %, Grand Rapids oil sands also have shallower bitumen absorption features. These spectral differences may be attributed to the mineralogical differences between the two formations, namely, structural water in K-feldspar. Hyperspectral analysis techniques that have been previously developed for McMurray oil sands were tested on a Grand Rapids Formation core. Using contrast stretching and the three-band combination of R e 2.05 μm, G e 2.13 μm, and B e 2.28 μm (where R, G, and B refer to red, green, and blue channels), the visibility of sedimentological features was enhanced in SWIR imagery. With a simple recalibration, an established spectral model for the estimation of total bitumen content (TBC) produced results in close agreement with Dean–Stark analysis data (coefficient of determination [R2] e 0.92, ±1.1 wt. %) and of similar accuracy to what has been previously demonstrated for the McMurray Formation. The results of this study suggest that with minor adjustments, oil sands spectral analysis techniques are transferable between different geological formations.

TREE SPECIES RECOGNITION IN SPECIES RICH AREA USING UAV-BORNE HYPERSPECTRAL IMAGERY AND STEREO-PHOTOGRAMMETRIC POINT CLOUD

Abstract. Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with the original uncalibrated imagery. Alternative estimators were tested for predicting tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. All tested estimators gave similar trend in the results: the calibrated reflectance values performed better in predicting tree species and genus compared to uncorrected hyperspectral pixel values. Furthermore, the combination of VNIR, SWIR and 3D features performed better than any of the data sets individually, with calibrated reflectances and original pixel values alike. The highest proportion of correctly classified trees was achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features: 0.823 for tree species and 0.869 for tree genus.

Gaze-Contingent Center-Surround Fusion of Infrared Images to Facilitate Visual Search for Human Targets

We investigated gaze-contingent fusion of infrared imagery during visual search. Eye movements were monitored while subjects searched for and identified human targets in images captured simultaneously in the short-wave (SWIR) and long-wave (LWIR) infrared bands. Based on the subject's gaze position, the search display was updated such that imagery from one sensor was continuously presented to the subject's central visual field ("center") and another sensor was presented to the subject's non-central visual field ("surround"). Analysis of performance data indicated that, compared to the other combinations, the scheme featuring SWIR imagery in the center region and LWIR imagery in the surround region constituted an optimal combination of the SWIR and LWIR information: it inherited the superior target detection performance of LWIR imagery and the superior target identification performance of SWIR imagery. This demonstrates a novel method for efficiently combining imagery from two infrared sources as an alternative to conventional image fusion. © 2017 Her Majesty the Queen in Right of Canada.

Gaze-Contingent Center-Surround Fusion of Infrared Images to Facilitate Visual Search for Human Targets

We investigated gaze-contingent fusion of infrared imagery during visual search. Eye movements were monitored while subjects searched for and identified human targets in images captured simultaneously in the short-wave (SWIR) and long-wave (LWIR) infrared bands. Based on the subject’s gaze position, the search display was updated such that imagery from one sensor was continuously presented to the subject’s central visual field (“center”) and another sensor was presented to the subject’s non-central visual field (“surround”). Analysis of performance data indicated that, compared to the other combinations, the scheme featuring SWIR imagery in the center region and LWIR imagery in the surround region constituted an optimal combination of the SWIR and LWIR information: it inherited the superior target detection performance of LWIR imagery and the superior target identification performance of SWIR imagery. This demonstrates a novel method for efficiently combining imagery from two infrared sources as an alternative to conventional image fusion.

Characterizing Kimberlite Dilution by Crustal Rocks at the Snap Lake Diamond Mine (Northwest Territories, Canada) using SWIR (1.90–2.36μm) and LWIR (8.1–11.1μm) Hyperspectral Imagery Collected from Drill Core

Short-wave infrared (SWIR, 1.90–2.36 μm) and long-wave infrared (LWIR, 8.1–11.1 μm) hyperspectral images collected using the SisuROCK system were used to develop an automated methodology for generating kimberlite dilution maps. Smoothed and denoised images from two Snap Lake (Northwest Territories, Canada) kimberlite drill cores were processed, and SWIR and LWIR spectral endmembers were extracted from the images with each mineralogical endmember assigned to one of four compositional groups: undiluted kimberlite, microdiluted kimberlite, micro- and macrodiluted kimberlite, and crustal rocks. These endmembers were used to classify the SWIR and LWIR images, and the results were validated using linescan data, drill core logs, petrology reports, and the results of X-ray diffraction, Raman spectroscopy, and Micro-Fourier Transform Infrared (FTIR) spectroscopy. This study demonstrates that hyperspectral imagery can be used to generate dilution maps for hypabyssal kimberlites that far supersede other current techniques in terms of spatial resolution.

An in-depth simulation of EnMAP acquisition geometry

The future hyperspectral satellite EnMAP (Environmental Mapping and Analysis Program) uses two separate sensors for the acquisition of VNIR and SWIR imagery. Due to their geometric configuration, the SWIR and VNIR instruments map the same positions on the ground with a time delay of 88ms. Coupled with attitude controller inaccuracies this leads to an estimated co-registration error between SWIR and VNIR higher than the maximum 0.2 pixels designated in the specifications of EnMAP imagery. It is assumed that, by approximating or interpolating the real attitude and geometrically correcting the images, this co-registration error can be significantly reduced. To validate these assumptions, a geometric simulator was developed at the German Aerospace Center DLR which is responsible for the development of the ground segment of EnMAP. The implemented simulator, together with an evaluation of the absolute and relative accuracy, performed using this simulator, are presented in this article. The obtained results demonstrate that the desired co-registration accuracy between SWIR and VNIR imagery can be achieved by using Spline or Chebyshev approximation for the attitude reconstruction but not by using Lagrange interpolation.

Evaluation of SWIR-based methods for quantifying active volcano radiant emissions using NASA EOS-ASTER data

Analysis of thermally emissive volcanic features using satellite infrared remote sensing has been conducted over recent decades, primarily using shortwave and thermal infrared (SWIR; TIR) radiance data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), mounted on the Earth Observation System (EOS) Terra satellite, offers an advance on earlier instruments, having more bands covering the SWIR atmospheric window and offering a wider dynamic range. This paper compares methods used to analyse ASTER SWIR imagery of active volcanoes, using both simulated cases and actual ASTER imagery of Lascar Volcano, and focuses on radiative power estimates. Those based on the Oppenheimer approach are found to be most reliable for simulated surfaces, with the Lombardo and Buongiorno and Dozier retrievals having larger uncertainties in most cases. However, the Dozier Method results in the highest proportion of successful retrievals, the reliability of which is influenced by factors including band combination, gain setting and saturation. The radiative power metric is shown as a more reliable measure than sub-pixel characterisations of hotspot temperature and area, as retrieved by these methods. We conclude with an assessment of ASTER in terms of its utility for providing quantitative observations of active volcanic surfaces.

Mapping Vegetation, Soils, and Geology in Semiarid Shrublands Using Spectral Matching and Mixture Modeling of SWIR AVIRIS Imagery

Spectral matching and linear mixture modeling techniques have been applied to synthetic imagery and AVIRIS SWIR imagery of a semiarid rangeland in order to determine their effectiveness as mapping tools, the synergism between the two methods, and their advantages, and limitations for rangeland resource exploitation and management. Spectral matching of pure library spectra was found to be an effective method of locating and identifying endmembers for mixture modeling although some problems were found with the false identification of gypsum. Mixture modeling could accurately estimate proportions for a large number of materials in synthetic imagery; however, it produced high variance estimates and high error estimates when presented with all nine AVIRIS endmembers because of high noise levels in the imagery. The problem of which endmembers to select was addressed by implementing a mixture model that allowed estimation of the errors on the proportions estimates, discarding the endmembers with the highest errors, recomputing the errors, and the proportions estimates, and iterating this process until the mixture maps were relatively free from noise. This methodology ensured that the lowest contrast materials were discarded. The inevitable confusion that followed was monitored the using the maps produced by spectral matching. Spectral matching was more effective than mixture modeling for geological mapping because it allowed identification and mapping of the relatively pure regions of all the surficial materials that exert an influence on the spectral response. The maps of the different clay minerals were of considerable value for mineral exploration purposes. Conversely, spectral matching was less useful than mixture modeling for rangeland vegetation studies because a classification of all pixels is needed and abundance estimates are required for many applications. Mixture modeling allowed identification of both nonphotosynthetic and green vegetation cover and thus total cover. Though the green vegetation mixture map appears to be very precise, the nonphotosynthetic vegetation estimates were poor.