Multi-spectral and multitemporal MODIS remote sensing imagery classification based on MNF transform and grayscale morphological filter in Sanjiang plain

By analyzing spectral characteristics and phenological characteristics of remote sensing images, we select multi-temporal NDVI data along with other useful bands for data processing. By performing an enhanced Lee filter and MNF transform on the data, the features of different land use types has been enhanced. Then, a morphological filtering is conducted on the data to extract dry land. Finally, we separate wetland and paddy field by a SOM neural network clustering. As a result, the accuracy of land use type classification has been improved.