01961nas a2200217   4500008004100000245010700041210006900148260000900217300001400226490000600240520123400246653002201480653001801502653003201520653001901552653001901571100001201590700001201602700001201614856011701626       2007                        eng d00aMulti-source remote sensing classification based on Mallat fusion and residual error feature selection0 aMultisource remote sensing classification based on Mallat fusion  c2007  a130 - 1370 v53 aClassification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.10aFeature selection10aMallet fusion10aMulti-source classification10aNeural network10aResidual error1 aCao, D.1 aYin, Q.1 aGuo, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350668378&partnerID=40&md5=8e01a98aa0645ce6934545475591db77