Long Range Automatic Detection of Small Targets in Sequences of Noisy Thermal Infrared Images

In this paper, an approach to the automatic detection of vehicles at long range using sequences of thermal infrared images is presented. The vehicles in the sequences can be either moving or stationary. The sensor can also be mounted on a moving platform. The targetarea in the images is very small, typically less than 10 pixels on target. The proposed method consists of two independent parts. The first part seeks for possible targets in individual images and then merges the results for a subsequence of images. The decision for this part of the algorithm is based on temporal and spatial consistency of the targets through the considered image subsequence. The second part of the algorithm specifically focuses on finding moving objects in the scene. Clearly, as the sensor may itself be moving too, the effect of this motion on the images has to be eliminated first. This was done using a model based registration technique. The algorithm proposed in this paper was implemented and tested on a ..

Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity

Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many anomaly detectors have been proposed in the literature. They differ in the way the background is characterized and in the method used for determining the difference between the current pixel and the background. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the background of the complete scene by a single multivariate normal probability density function. In many cases, this model is not appropriate for describing the background. For that reason a variety of other anomaly detection methods have been developed. This paper examines three classes of anomaly detectors: subspace methods, local methods, and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with diverse complexity. The results are evaluated and compared