A Comparative Study of Noise Sensitivity on Different Hyperspectral Classification Methods

Hyperspectral image classification has been a constant hot topic in remote sensing field, and achieved significant progress recently. Until now, most of the existing works are based on high-quality noise-free datasets, whereas in real applications, the images are often degraded by different types of noise, which makes the noise sensitivity become one of the key issues for classification assessment. In this paper, we study the noise effects on hyperspectral image classification including Guassian, salt-and-pepper, and stripe noise. The experimental results shows that noise has varying degrees of negative effects on different hyperspectral classification methods, which provides instructional information for method design and selection under noise environment in actual classification applications.