Remote sensing is often used for detection of predefined targets, such as vehicles, man-made objects, or other specified objects. We describe a new technique that combines both spectral and spatial analysis for detection and classification of such targets. Fusion of data from two sources, a hyperspectral cube and a high-resolution image, is used as the basis of this technique. Hyperspectral imagers supply information about the physical properties of an object while suffering from low spatial resolution. The use of high-resolution imagers enables high-fidelity spatial analysis in addition to the spectral analysis. This paper presents a detection technique accomplished in two steps: anomaly detection based on the spectral data and the classification phase, which relies on spatial analysis. At the classification step, the detection points are projected on the high-resolution images via registration algorithms. Then each detected point is classified using linear discrimination functions and decision surfaces on spatial features. The two detection steps possess orthogonal information: spectral and spatial. At the spectral detection step, we want very high probability of detection, while at the spatial step, we reduce the number of false alarms. Thus, we obtain a lower false alarm rate for a given probability of detection, in comparison to detection via one of the steps only. We checked the method over a few tens of square kilometers, and here we present the system and field test results.