Woodland Segmentation of Gaofen-6 Remote Sensing Images Based on Deep Learning

Gaofen-6 (GF-6) is a geostationary, earth-observation satellite, rely on it's multi-spectral images, GF-6 has the ability to support the monitoring of woodland resources. In this paper, the multi-spectral images sent by GF-6 are studied as dataset, and a model called Infrared Attention Network (InfAttNet) which based on semantic segmentation method is proposed to distinguish woodland from other land types to achieve the purpose of woodland extraction. To make full use of the spectral information, InfAttNet has an additional encoder to extract the features of infrared bands independently. Besides, infrared attention blocks help InfAttNet to enhance the characteristics of woodland. The experimental results proved that InfAttNet improves the accuracy of woodland extraction, and the segmentation effect is strengthened compared with classical networks.