Object-level processing of spectral imagery for detection of targets and changes using spatial-spectral-temporal techniques.

Automatic detection of ground targets and their movements is an important problem in military remote sensing. Much recent attention has been afforded the exploitation of spectral imagery for this application. However, current spectral detection algorithms yield inadequate performance in many demanding scenarios. The present work explores several techniques founded on the notion of object-level image processing. In object-level processing we seek to progress from a pixel-level image description to a description at the spatial scale of natural objects. The concept of a natural object is inspired by the human visual system. An important advancement of a recently reported spectra object extraction method is presented. This technique, Knowledge Based Object Reassembly, extracts objects based on the spectral similarity of their pixels and then merges spatially adjacent objects according to a maximum classification confidence criterion. The improvement in object extraction allows the accurate characterization of natural objects by a spatial-spectral feature set. This feature set then forms the basis of detection, classification and change detection algorithms. The performance of the technique is assessed and its impact on spectral object level change detection and spatial-spectral object-level target discrimination and classification is measured. Both multi-spectral and hyper-spectral imagery over several spectral regions is analyzed.