Spectral quality equation relating collection parameters to object/anomaly detection performance

Abstract

As hyperspectral remote sensing technology migrates into operational systems, there is an urgent need to understand the phenomenology associated with the collection parameters and how they relate to the quality of the information extracted from the spectral data for different applications. If such relationships can be established, data collection requirements and tasking strategies can then be formulated for these applications. This paper describes a functional expression or spectral quality equation that has been established for object/anomaly detection in the reflective region (0.4 to 2.5 microns) of the spectrum. This spectral quality equation relates the collection parameters (i.e. spatial resolution, spectral resolution, signal-to-noise ratio, and scene complexity) to the probability of correct detection (Pd) for object/anomaly detection at a given probability of false alarms (Pfa). Follow-on work will be performed to establish a spectral quality equation for material identification.

Small object hyperspectral detection from a low-flying UAV

“…Specifically, we use a simulation technique to perform a trade-study of RX detection results as a function of both the spatial resolution and atmospheric distortion that result from increasing the stand-off distance between sensor and scene. Ground sample distance as a function of stand-off distance is relevant in this study, so we build on the work of previously published papers that addressed issues related to hyperspectral detection as a function of GSD 3,4 . While we build upon that work, we also present data in a different regime where both the detected objects we study are smaller and the sensor GSD is smaller than in previous work.…”

“…While we build upon that work, we also present data in a different regime where both the detected objects we study are smaller and the sensor GSD is smaller than in previous work. Additionally, we address issues of atmospheric distortion, which were assumed negligible in the aforementioned previously published papers 3,4 . With the simulation method presented here, we study two HSI data sets where the measured GSD is small.…”

Spectral Image Utility Sensitivity to Image Preprocessing and Information Exploitation Parameters

“…This image-derived method differed from other spectral image utility approaches [2][3] in that it did not require prior knowledge of image acquisition parameters, provided a self-contained means to both assess and predict spectral image utility, and derived required information from a specific image rather than a notional statistical description of the image or sensor characteristics. We calculated utility by sampling the receiver operating characteristic (ROC) curve to obtain a probability of detection associated with a specified false alarm probability (PFA).…”

Spectral quality metrics for VNIR and SWIR hyperspectral imagery

“…There have been other efforts at assigning a quantitative quality measure to hyperspectral imagery including ones based on analysts' interpretation of quality and utility [1] [2], spectral similarity [3], and the relationship of detection and false alarm probabilities with collection parameters [4]. These efforts offer alternative approaches to this difficult problem and demonstrate promise in the context of their work.…”