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  • 1
    Publication Date: 2019-07-13
    Description: The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Annual Symposium on Machine Processing of Remotely Sensed Data; Jun 21, 1977 - Jun 23, 1977; West Lafayette, IN
    Format: text
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  • 2
    Publication Date: 2019-07-13
    Description: The probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Symposium on Machine Processing of Remotely Sensed Data; Jun 29, 1976 - Jul 01, 1976; West Lafayette, IN
    Format: text
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