As the amount of multidimensional remotely sensed data is growing tremendously, Earth scientists need more efficient ways to search and analyze such data. In particular, extracting image content is emerging as one of the most powerful tools to perform data mining. One of the most promising methods to extract image content is image classification, which provides a labeling of each pixel in the image. In this paper, we concentrate on neural classifiers and show how information obtained through wavelet transform can be integrated in such a classifier. After a systematic dimensionality reduction by a principal component analysis technique, we apply a local spatial frequency analysis. This local analysis with a composite edge/texture wavelet transform provides statistical texture information of the landsat imagery testset. The network is trained with both radiometric landsat/thematic mapper bands and with the additional texture bands provided by the wavelet analysis. The paper describes the type of wavelets chosen for this application, and several sets of results are presented.
|Original language||American English|
|Title of host publication||AeroSense'97|
|Publisher||International Society for Optics and Photonics|
|State||Published - 1997|