期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2006
卷号：XXXVI Part 7
出版社：Copernicus Publications
摘要：Image classification can benefit from incorporating texture by enabling an increased number of classes and improving thematic accu- racy. Incorporating texture also involves special attention in a number of aspects that range from the texture source to the evaluation of accuracy through pre-processing, training strategy and choosing a texture extraction paradigm and a classifier. Without special care in these aspects, classification results can be very unpredictable, especially when mixing spectral and textural features in the classification. This is mainly due to the spatial dependency of texture features. The present article aims at analyzing these aspects (six in all) through a review of the concepts involved and a demonstration with two sample image data sets in a complex semiarid environment in Brazil. The data sets were formed with texture features from a SPOT-5 panchromatic image and spectral features from LANDSAT 7 ETM+ data. Results suggest that useful texture features can be extracted from SPOT-5 panchromatic data and that a mixed classification scheme is generally better than either approaches (spectral or textural). They also suggest that a non parametric classifier (Fisher linear discriminant) performs better for sets incorporating spectral and textural features and is less affected by edges and borders
关键词：Land Cover; Classification; Texture; Vision; Landsat; SPOT; High resolution
