Object detection by step-wise analysis of spectral, spatial, and topographic features

In many computer vision systems accurate identification of various objects appearing in a scene is required. In this paper we address the problem of object detection in analyzing high resolution multispectral aerial images. Development of a practical object detection approach should consider issues of speed, accuracy, robustness, and amount of supervision allowed. The approach is based upon extraction of information from images and their systematic analysis utilizing available prior knowledge of various physical attributes of the objects. The step-wise approach examines spectral, spatial, and topographic features in making the object vs background decision. Techniques for the analysis of the spectral, spatial, and topographic features tend to be of increasing levels of computational complexity. The computationally simpler spectral feature analysis is performed for the entire image to detect candidate object regions. Only these regions are considered in the spatial feature analysis step to further reduce the number of candidate regions which need to be analyzed in the topographic feature analysis step. Such step-wise analysis makes the entire object detection process efficient by incorporating the process of “focus of attention” to identify regions of interest thus eliminating a relatively large portion of image from further detailed examination at every stage. Results of the experiments performed using several high resolution multispectral images have demonstrated the basic feasibility of the approach. The images utilized in the experiments are acquired from geographically different locations, at different times, with different types of background, and are of different resolution. Successful object detection with high accuracy and low false alarm rates indicate the robustness of this approach.