Coffee Crop Recognition Using Multi-scale Convolutional Neural Networks

Abstract

Identifying crops from remote sensing images is a fundamental to know and monitor land-use. However, manual identification is expensive and maybe impracticable given the amount data. Automatic methods, although interesting, are highly dependent on the quality of extracted features, since encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models. Even though many visual descriptors have been proposed or successfully used to encode spatial features, in some cases, more specific description are needed. Deep learning has achieved very good results in some tasks, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data In this paper, we propose two multi-scale methods, based on deep learning, to identify coffee crops. Specifically, we propose the Cascade Convolutional Neural Networks, or simply CCNN, that identifies crops considering a hierarchy of networks and, also, propose the Iterative Convolutional Neural Network, called ICNN, which feeds a same network with data several times. We conducted a systematic evaluation of the proposed algorithms using a remote sensing dataset. The experiments show that the proposed methods outperform the baseline consistent of state-of-the-art components by a factor that ranges from 3 to 6%, in terms of average accuracy.