高空间分辨率遥感图像分类的SSMC方法

SSMC Method for the Classification of High Spatial Resolution Remotely Sensed Images

近年来随着高空间分辨率遥感的发展,影像的空间细节描述能力得到提高,像元之间的空间相关性得到增强,使传统的遥感影像光谱分类方法面临着巨大的挑战。基于此背景,提出了高分辨率遥感影像分类的SSMC(spatial and spectralm ixed c lassifier)方法,旨在同时采用光谱和空间特征进行遥感影像分类。本文是基于SSMC方法的一个具体的实验,通过多尺度的空间金字塔构造每个像元的空间参数,整合影像的光谱信号和空间信息进行高分辨率遥感影像分类。实验结果证明,SSMC方法对于提高高分辨率遥感影像的分类精度具有积极的意义。

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 author presented a new mothod called SSMC（ Spatial and Spectral Mixed 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 set of windows, 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 to make a multiresolution 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.