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Widely used methods of spectral clustering, target, and anomaly detection when applied to spectral imagery provide less than desirable results across sensor type, scene content, spectral and spatial resolutions due to the complex nature of the data. This results in a large burden placed on the analyst in terms of the amount of data needed to be processed and the ability to discern the difference between “interesting” and “uninteresting” regions in the imagery. For this research, a variety of data driven algorithms for spectral image analysis are applied to spatial tiles of a large area scene. A feature map is created by assigning a metric determined for each algorithm result to each spatial tile. The feature maps are organized into a tiled, multi-band feature image. Two visualization methods introduced here provide a detection map which can cue image analysts to visually inspect locations within a large area scene with a high likelihood of containing interest. Unsupervised classification is applied to this feature image such that the image is divided into segments representing either “interesting” or “not interesting” content with the tile. False-color visualization of three independent metrics is also presented as a way to indicate the type and strength of the amount of interest within a tile.