Temporal and spatial compression of infrared imagery sequences containing slow moving point targets

Infrared imagery sequences are used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research focuses on slow moving point targets that are less than one pixel in size, such as aircraft at long ranges from a sensor. Since transmitting infrared (IR) imagery sequences to a base unit or storing them consume considerable time and resources, a compression method which maintains the point target detection capabilities is desired. In our previous work, we introduced two temporal compression methods, which preserve the temporal profile properties of the point target, in the form of the discrete cosine transform (DCT) quantization and the parabola fit. In the present work, we continue the compression task method of the DCT quantization by applying spatial compression over the temporally compressed coefficients, followed by bit encoding. We evaluate the proposed compression methods using an SNR-based measure for point target detection. Furthermore, we introduce an automatic detection algorithm of the target tracks that extracts the target location from the SNR scores image, which is acquired during the evaluation process. We previously determined that it is necessary to establish a minimal noise level in the SNR-based measure, to compensate for smoothing that is induced by the compression. Here, the noising process is modified, in order to allow detection of targets traversing all background types.