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Vegetation Recognition based on Deep Learning with Feature Fusion

Vegetation recognition is an important task for optical remote sensing image parsing, providing information on vegetation cover for disaster monitoring, natural environment change monitoring, etc. In recent years, deep learning methods have driven a great advance in object recognition. Convolutional neural network (CNN) is a typical deep learning method which can recognize vegetation objects from optical remote sensing images. However, CNN methods exploit only high-level features for vegetation recognition and do not consider multi-scale vegetation objects, and thus cannot achieve high-accuracy pixel-wise semantic segmentation of vegetation objects from complex optical remote sensing images at various resolutions. To deal with these problems, a vegetation recognition method based on a deep fully convolutional network with feature fusion (FCN-FF) is proposed in this paper. The FCN-FF extracts hierarchical features from optical remote sensing images, and then fuses high-level and low-level features from a deep layer and a shallow layer for vegetation recognition. The fusion of different levels of feature improves recognition accuracy of vegetation from complex images. Furthermore, the FCN-FF selects vegetation objects with various spatial resolutions as training samples and thus it is suitable to multi-scale vegetation objects. In this study, a validation experiment and a comparative experiment are performed. The experimental results show that our method achieves high-accuracy pixel-wise vegetation recognition from complex optical remote sensing images.