Learning Deep Features on Multiple Scales for Coffee Crop Recognition

摘要

Geographic mapping of coffee crops by using remote sensing images and supervised classification has been a challenging research subject. Besides the intrinsic problems caused by the nature of multi-spectral information, coffee crops are non-seasonal and usually planted in mountains, which requires encoding and learning a huge diversity of patterns during the classifier training. In this paper, we propose a new approach for automatic mapping coffee crops by combining two recent trends on pattern recognition for remote sensing applications: deep learning and fusion/selection of features from multiple scales. The proposed approach is a pixel-wise strategy that consists in the training and combination of convolutional neural networks designed to receive as input different context windows around labeled pixels. Final maps are created by combining the output of those networks for a non-labeled set of pixels. Experimental results show that multiple scales produces better coffee crop maps than using single scales. Experiments also show the proposed approach is effective in comparison with baselines.