11. An apparatus for classifying and sorting input data in a data stream, comprising: a processor having a first input for receiving said input data, a second input, and an output, and wherein said processor includes: a classifier input control including said first input and said second input; an adaptive classifier; a ground truth data input; a ground truth resampling buffer; and a source data re-sampling buffer, wherein said processor is configured for: a) sampling the input data with the input control; b) comparing one or more classes of the sampled input data with initially preset data classifications that constitute a baseline for determining mis-classification of data patterns; c) determining a degree of mis-classification of the input data and assigning a probability proportional to the degree of misclassification as a criterion for entry into a resampling buffer thereto; d) entering data patterns causing mis-classification in a resampling buffer with a probability value proportional to the degree of mis-classification; e) comparing the data patterns to a ground truth source and aligning the data patterns with their associated data pattern labels employing the same decision outcome based on a mis-classification probability as applied to the resampling buffer to form a set of training data; and f) updating the adaptive classifier to correlate with the training data; and g) repeating steps a)-f) until a sufficient degree of data classification optimization is realized; whereby an optimized data stream is provided by said processor at said output.
11. An apparatus for classifying and sorting input data in a data stream, comprising: a processor having a first input for receiving said input data, a second input, and an output, and wherein said processor includes: a classifier input control including said first input and said second input; an adaptive classifier; a ground truth data input; a ground truth resampling buffer; and a source data re-sampling buffer, wherein said processor is configured for: a) sampling the input data with the input control; b) comparing one or more classes of the sampled input data with initially preset data classifications that constitute a baseline for determining mis-classification of data patterns; c) determining a degree of mis-classification of the input data and assigning a probability proportional to the degree of misclassification as a criterion for entry into a resampling buffer thereto; d) entering data patterns causing mis-classification in a resampling buffer with a probability value proportional to the degree of mis-classification; e) comparing the data patterns to a ground truth source and aligning the data patterns with their associated data pattern labels employing the same decision outcome based on a mis-classification probability as applied to the resampling buffer to form a set of training data; and f) updating the adaptive classifier to correlate with the training data; and g) repeating steps a)-f) until a sufficient degree of data classification optimization is realized; whereby an optimized data stream is provided by said processor at said output. 18. An apparatus as in claim 11 , wherein the data comprises remote sensing data.