An in-depth analysis of hyperspectral target detection with shadow compensation via LiDAR

Shadowy areas present a big hindrance in target detection from HSI, as the reflectance data received from target materials is significantly diminished when measured from shadowy areas. In this work, we perform an in-depth analysis of hyperspectral target detection on targets in full illumination and in partial or full shadows; and analyze how much target detection can be improved if the hyperspectral data is corrected at the regions of shadows. To do this, first, we detect the shadows using LiDAR, and propose a way to correct them in the hyperspectral image using the physical radiance model. Then, using three target detectors, namely the spectral angle mapper (SAM), adaptive coherence estimator (ACE) and matched filter (MF), we compare the results of target detection with and without shadow correction. We analyze our results based on the target material (red felt and blue felt targets), the background (grass or gravel), the amount of shadow (partial or full) and based on the time of the data collection (in the morning or at noon). Our results indicate several interesting observations: (i) the red-felt material is much harder to detect than the blue-felt material even though they are made up of the same material; but this gap in detection decreases significantly if shadow correction is performed using the radiance model, (ii) both the red-felt and the blue-felt targets are hard to detect earlier in the day when the rays from the sun are inclined; but there is not a significant difference in making the data collection in the morning or at noon if shadow correction is performed, and (iii) the shadow compensation dramatically increases the detection rates and boosts up the area under the receiver operating curve (AUC) from around 0,7-0,9 band to the 0,95-1,00 band. In addition, we provide our shadow detection code¹, sky-view factor results and all the MODTRAN outputs for the parts of the Share2012 dataset used in this work. In doing so, we hope to provide a benchmark for researchers who would like to test their target detection or shadow correction algorithms on HSI-LiDAR data.