A wavelet-based classification of hyperspectral images using Schroedinger eigenmaps

作者：Ghasrodashti Elham Kordi; Helfroush Mohammad Sadegh^*; Danyali Habibollah

来源：International Journal of Remote Sensing, 2017, 38(12): 3608-3634.

DOI：10.1080/01431161.2017.1302108

This article proposes a new algorithm for hyperspectral image classification. The proposed method is a spectral-spatial method based on wavelet transforms, kernel minimum noise fraction (KMNF) and spatial-spectral Schroedinger eigenmaps (SSSE). To overcome the computation complexity, one-dimensional discrete wavelet transform (1D-DWT) is applied in spectral domain. To reduce noise, KMNF coefficients are extracted in wavelet space. To solve time-consuming problem, 2D-DWT coefficients are employed in spatial space. Hence, the combination of 1D-DWT, KMNF, and 2D-DWT is suggested to create SSSE features. The classification is carried out by a Support Vector Machine (SVM) classifier. Experimental results show that classification accuracy and time consumption are effectively improved compared to the state-of-the art reported spectral-spatial SVM-based methods.

• 出版日期2017