  We introduce a soft computing approach for automatically selecting and
combining indices from remote sensing multispectral images that can be used for
classification tasks. The proposed approach is based on a Genetic-Programming
(GP) framework, a technique successfully used in a wide variety of optimization
problems. Through GP, it is possible to learn indices that maximize the
separability of samples from two different classes. Once the indices
specialized for all the pairs of classes are obtained, they are used in
pixelwise classification tasks. We used the GP-based solution to evaluate
complex classification problems, such as those that are related to the
discrimination of vegetation types within and between tropical biomes. Using
time series defined in terms of the learned spectral indices, we show that the
GP framework leads to superior results than other indices that are used to
discriminate and classify tropical biomes.
