19. A method for object detection in remotely-sensed images, the method comprising: identifying a first domain within a remotely-sensed image, wherein the first domain comprises textural features and spectral features; learning a textural relationship between the textural features within the first domain; training a first classifier to detect a first object based on the learned textural relationships between the textural features within the first domain; learning a joint relationship between the textural features and the spectral features within the first domain for the first object; training a second classifier to detect the first object based on the learned textural relationship and the learned joint relationship; learning a classifier relationship between the trained first classifier and the trained second classifier, wherein learning the classifier relationship includes: establishing a textural activation potential for the first classifier corresponding to at least one object; establishing a spectral activation potential for the second classifier corresponding to at least one object; and learning a relationship between the first classifier and the second classifier based on the established textural activation potential and the established spectral activation potential; training an object detector to detect at least one object based on the learned classifier relationship; performing a secondary optimization process to optimize the fast object detector; and detecting the at least one object within the second domain using the optimal fast object detector.
20. The method of claim 19 , wherein the optimization process comprises establishing pooling centers to incrementally improve object detection.