Advancing Cave Detection Using Terrain Analysis and Thermal Imagery

Abstract

Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.

Fundamental Science and Engineering Questions in Planetary Cave Exploration

“…Our collective knowledge has progressed from musing to confirming the presence of SAPs beyond Earth—not only on the Moon, but on at least 10 other planetary bodies (Wynne et al, 2022 ). To date, detection capabilities have been developed and refined (e.g., Chappaz et al., 2017 ; Cushing, 2017 ; Pisani & De Waele, 2021 ; Wynne et al., 2008 , 2021 ), and astrobiological sampling techniques and associated instrumentation suites have been advanced by several key research projects (e.g., Agha‐Mohammadi et al., 2021 ; Blank, 2020 ; Uckert et al., 2020 ). Moreover, a bevy of robotic platforms are slowly moving toward flight‐qualified status (see Robotics section above), while astronauts are now being trained to conduct subsurface missions (Sauro et al., 2021 ).…”

Using near–surface temperature data to vicariously calibrate high-resolution thermal infrared imagery and estimate physical surface properties

“…The use of thermal cameras to analyze landscapes has expanded over the last decade (e.g., [ 6 , 14 , 15 , 21 , [32] , [33] , [34] , 39 , 40 ]). However, depending on the type and age of the thermal cameras, the output image may be self-calibrated in radiance, corrected for atmospheric absorption and emission, and each pixel converted to surface temperature values.…”

“…Several experiments to analyze cave-bearing landscapes in the Mojave Desert, California, assisted in this instrument's maturation. Wynne et al., [ 40 , 42 ] reported thermal distinctions between cave, tunnel cave (i.e., a subterranean feature with entrances on either end, typically with frequent air flow) and random non-cave locations on the surface in thermal images captured at 10‐minute intervals over a 24‐hour period. Their results demonstrated how larger caves may be distinguishable from shallow alcoves and tunnel features.…”

“…The methods presented here were prompted by a QWIP-based airborne thermal imagery acquisition mission conducted in 2011 (refer to [ 41 , 42 ] for details). As the acquired imagery were not radiometrically calibrated and atmospherically corrected, we needed to confirm the Digital Number (DN) pixel values were valid as input into algorithms for detecting terrestrial caves.…”

“…As the acquired imagery were not radiometrically calibrated and atmospherically corrected, we needed to confirm the Digital Number (DN) pixel values were valid as input into algorithms for detecting terrestrial caves. Fortunately, we had previously collected a 2010 dataset of QWIP ground-based thermal imagery and in situ near surface kinetic temperature data [ 34 , 35 ], as a precursor experiment to the overflight campaign [ 16 , 42 ]. We used these data to vicariously calibrate thermal infrared imagery, as well as estimate physical surface properties.…”