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1) for the surface spatial and spectral variations ␳(x, y, ␭). The cos[␪(x, y)] term is a topographic effect that is described in the next section. The path radiance term Lh(␭) is primarily of concern at short, blue-green wavelengths, and the transmittance terms Ts(␭) and Tv(␭) are usually ignored for coarse multispectral sensing, such as with Landsat TM, where the bands are placed within atmospheric ‘‘windows’’ of relatively high and spectrally flat transmittance. For hyperspectral data, however, knowledge of and correction for transmittance is usually required if the data are to be compared to reflectance spectra measured in a laboratory.

Fewer hidden layer nodes result in faster BP training. Performance Comparison to Statistical Classifiers The ANN type of classifier has some unique characteristics that are important in comparing it to other classifiers: 1. , they will vary from run to run on the same training data). It has been estimated that this variation is as much as 5% (17). 2. The decision boundaries move in the feature space to reduce the total output error during the optimization process. The network weights and final classification map that result will depend on when the process is terminated.

A wide range of network architectures have been proposed (14); here a simple threelayer network is considered to explain the basic operation. The input nodes do no processing but simply provide the paths for the data into the hidden layer. Each input node is connected to each hidden layer node by a weighted link. In the hidden layer, the weighted input features are summed and compared to a thresholding decision function. The decision function is usually ‘‘soft,’’ with a form known as a sigmoid, output(input) = 1 1 + exp(−input) (18) The output from each hidden layer node is then fed through a weighted link to each output layer node.

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