Abstract
The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.
Highlights
A high-fidelity hyperspectral imagery contains crucial spatial and spectral information of a given scene
Our algorithm is tested on the Selene SCIH23 dataset [29] which was acquired by the Defence Science and Technology Laboratory (DSTL) covering 0.4 to 2.5 μm
Construction of Ground truth (GT) for the Selene scene enables us to provide a more quantitative validation for it by means of confusion matrix, while for Modesto, it is more qualitative and evaluated visually. We have considered both classical methods exploiting descriptor-based shadow detection from RGB input, as in Gevers [15], and Beril [10], and methods that take in multispectral radiance input, as in RGBN [13] requiring RGB and a single near infrared (NIR) band, and false-color shadow detection method, LULC [12], requiring five input bands within 0.3 μm to 2.5 μm
Summary
A high-fidelity hyperspectral imagery contains crucial spatial and spectral information of a given scene. They used the grayscale histogram of the image to detect shadows around the building using the Otsu algorithm [11], their algorithm is referred to as Beril’s algorithm in the results In another contribution, Teke et al [12] proposed a false color space consisting of red, green and near infrared (NIR) bands. Teke et al [12] proposed a false color space consisting of red, green and near infrared (NIR) bands They dropped the blue color because it contains scattered light and removing it will increase the contrast between shadow and non-shadow regions, and will facilitate detection. This section is divided into two parts—the first presents a general description of the RT equation highlighting relevant parameters and elaborating the sources of errors and their impact on this work, and the second establishes the proposed general relationship between variably illuminated regions based on the RT equation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
