Traditional spectral based methods have proven inadequate for the classification of high spatial resolution remotely sensed images

due to the lack of consideration of the spatial features of the data. In order to improve the classification effect of high spatial resolution remotely sensed data

the authorpresented a newmothod called SSMC(Spatial and Spectra lMixed Classifier) driven by integrated spatial and spectral features. In this algorithm

we use twofold pyramids to extract the spatial information of images

one is the background pyramid composed of a setofwindows

such as 64×64、32×32、16×16 and 8×8

which can mimic human perception in identifying objects of different scales; and the other is mallat pyramid

which is used here tomake amultiresolution analysis for every level of the background pyramid. The new algorithm is constructed for the classification of high resolution multispectral images

whose typical cases are IKONOS、Quick Bird etc. And in the experiments

the presented method can achieve better accuracy than the conventional ones

so we can conclude that the SSMC method is active and encouraging.