条带消除对于高光谱图像分类效果的影响——以CHRIS/PROBA图像为例

Effect of destriping on hyperspectral image classification,take CHRIS/PROBA as an example

以CHRIS/PROBA高光谱图像数据为例,使用非监督分类的ISODATA和监督分类的最大似然法、支持向量机等3种经典的图像分类算法,对消条带前后的图像分别开展分类实验,并对分类结果做了分析。实验结果表明,消条带处理可以较好地改善高光谱图像分类结果的目视效果,能够消除不同类别斑块边缘因条带而产生的"毛刺"现象,这对地物斑块的形状及几何分布敏感的研究(如景观生态学)至关重要;但消条带处理对于提高分类精度的效果并不显著,精度提高最大值不到2%。

This paper takes CHRIS/PROBA hyperspectral images as an example. Image classifications are carried out on these original and destriped images by both unsupervised algorithm（ISODATA）and supervised algorithm（Maximum Likelihood, ML and Support Vector Machine, SVM）, and the results are analyzed. The results show that, destriping can well improve the visual effect of the classification by eliminating the burr between patches of different classes. This is very important for some researches that focus on the shape, geometry and distribution, such as landscape ecology. However,destriping cannot significantly improve the precision of classification; the maximum value of accuracy improvment is less than2 %.