The paper outlines a new method for band selection derived from a multivariate normal mixture anomaly detection
method. The method consists in evaluating detection performance in terms of false alarm rates for all band
configurations obtainable from an input image by selecting and combining bands according to selection criteria
reflecting sensor physics. We apply the method to a set of hyperspectral images in the visible and near-infrared spectral
domain spanning a range of targets, backgrounds and measurement conditions. We find optimum bands, and investigate
the feasibility of defining a common band set for a range of scenarios. The results suggest that near optimal performance
can be obtained using general configurations with less than 10 bands. This may have implications for the choice of
sensor technology in target detection applications. The study is based on images with high spectral and spatial resolution
from the HySpex hyperspectral sensor.
Proc. SPIE. 6699, Signal and Data Processing of Small Targets 2007
KEYWORDS: Target detection, Mathematical modeling, Detection and tracking algorithms, Data modeling, Sensors, Clouds, Signal processing, Filtering (signal processing), Mathematical morphology, Global Positioning System
In this paper real data from divers is analyzed for detection and tracking purposes. There are two divers, one with
an open breathing system, and the other with a closed breathing system. The data were recorded from an active
90kHz narrowband multibeam imaging sonar. A target such as the open breathing system diver yields several
detections of air bubbles, and should be handled as an extended target. Modeling the extended target with
morphological operators like erosion and dilation is discussed and a model of the bubbles in the data association
is developed. For this data the MCA (Morphological Cell Averaging)-CFAR is implemented in cojunction with
an augmented probabilistic data association filter (PDAF) that incorporates an additional bubble model.
Proc. SPIE. 5573, Image and Signal Processing for Remote Sensing X
KEYWORDS: Target detection, Signal to noise ratio, Hyperspectral imaging, Detection and tracking algorithms, Data modeling, Sensors, Reflectivity, Performance modeling, Atmospheric modeling, RGB color model
We study material identification in a forest scene under strongly varying illumination conditions, ranging from open sunlit conditions to shaded conditions between dense tree-lines. The algorithm used is a physical subspace model, where the pixel spectrum is modelled by a subspace of physically predicted radiance spectra. We show that a pure sunlight and skylight model is not sufficient to detect shaded targets. However, by expanding the model to also represent reflected light from the surrounding vegetation, the performance of the algorithm is improved significantly. We also show that a model based on a standardized set of simulated conditions gives results equivalent to those obtained from a model based on measured ground truth spectra. Detection performance is characterized as a function of subspace dimensionality, and we find an optimum at around four dimensions. This result is consistent with what is expected from the signal-to-noise ratio in the data set. The imagery used was recorded using a new hyperspectral sensor, the Airborne Spectral Imager (ASI). The present data were obtained using the visible and near-infrared module of ASI, covering the 0.4-1.0 μm region with 160 bands. The spatial resolution is about 0.2 mrad so that the studied targets are resolved into pure pixels.