Cropnet: Deep Spatial-Temporal-Spectral Feature Learning Network for Crop Classification from Time-Series Multi-Spectral Images

Abstract

The Deep Learning (DL) methods can automatically extract features without artificial prior, provides an effective solution for multi-temporal crop classification. Convolutional Neural Networks (CNNs) have superb spatial-spectral feature extraction capabilities, but often lack consideration of the temporal relationship of multi-temporal images, while Recurrent Neural Networks (RNNs) can better learn the sequential variation pattern. This paper designed a deep spatial-temporal-spectral feature learning network (CropNet) combining the advantages of a deep spatial-spectral feature learning module and a deep temporal-spectral feature learning module for better feature extraction in crop classification from time-series remote sensing images. From the results, the proposed method has better crop classification effects from time-series multispectral images in our experimental areas compared with some common traditional machine learning approaches and common DL methods.