Adaptive signature kernel (ASK) for the Subpixel Classifier: automated scene-to-scene detection of changed targets

摘要

Abstract: Using a spectral signature derived from training data in one image to detect the material of interest in another image is not always successful using image normalization alone. Normalization can correct for scene-to-scene differences in atmospheric, sun angle, and sensor calibration effects, but it does not compensate for scene-to-scene changes in inherent target characteristics. It is generally difficult to predict what kind of change may occur and what its impact on the signature might be. Even when the expected variations in signature can be predicted, expanding a signature's tolerance or creating a family of signatures to accommodate the variance can reduce discrimination and produce unacceptable false alarm rates. Ignoring them can lead to missed detections. ASK has been developed for use with Subpixel Classifier, software that automatically detects these scene-to-scene changes and accordingly adapts the spectral signature to maintain strong detection performance. Subpixel Classifier is used to make target detections in an image, and the spectra of the detections are automatically analyzed to characterize the change and adapt the signature to the target condition(s) in the scene. An interactive functionality can take advantage of scene knowledge when available. !2