Fine Classification of Typical Farms in Southern China Based on Airborne Hyperspectral Remote Sensing Images

摘要

In the southern part of China, peculiar land fragmentation so that crop planting is characterized by small planting area of a single block, alternate cropping in multiple plots and diversified planting in space. Based on the unique crop planting characteristics in southern part of China, this paper take typical southern farm in Honghu City, Hubei Province as an example, adopting the platform of unmanned aerial vehicle (UAV) to carry hyperspectral imaging spectrometer to obtain the “double high” (high spectral and high spatial resolution) images at the same time. To complete the crop fine classification of ‘double high’ images, the CNN-CRF algorithm is proposed. The CNN-CRF algorithm acquires 91.5% accuracy with only 1% train samples on remote sensing images, which performs far better than most traditional classification approaches.