Splines and geometric modeling

Bajaj, Chandrajit L.; Evans, Susan B.

doi:10.1201/9781420035315.ch53

Cited by 1 publication

(1 citation statement)

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“…We developed TPI, slope, curvature, and unenhanced thermal imagery values for all known cave and non-cave surface locations for each imagery dataset. Because thermal signal strength of cave entrances may be described as a gradation of thermal influence strongest near the center of the cave entrance and diminishing toward the outer edges of the entrance, we attempted to capture this slope using bilinear interpolation [83]. Starting at the center of the cave entrance (0 m) and increasing by 0.5 m concentric intervals, we extracted pixel values from 0 to 3.5 m (Figure 4) and calculated the average for each distance interval.…”

Section: Study Sites and Pixel Averagingmentioning

confidence: 99%

Advancing Cave Detection Using Terrain Analysis and Thermal Imagery

et al. 2021

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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.

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Section: Study Sites and Pixel Averagingmentioning

confidence: 99%