3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Shunping Ji,Yun Shi,Yulin Duan,Anjian Xu,Chi Zhang

doi:10.3390/rs10010075

Abstract

This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.

Highlights

Benefiting from the huge number of high-quality spectral-temporal images captured from Earth observation satellites, or unmanned aerial vehicles (UAV), automatic crop classification [1,2] is becoming a fundamental technology for yield estimation, economic assessment, crop transportation, etc
In the 2015 tests, the 3D convolutional neural network (CNN) is slightly better when comparing to the 2D CNN and other methods
A novel method based on 3D CNN is introduced to crop classification using multi-temporal remote sensing images

Summary

Introduction

Benefiting from the huge number of high-quality spectral-temporal images captured from Earth observation satellites, or unmanned aerial vehicles (UAV), automatic crop classification [1,2] is becoming a fundamental technology for yield estimation, economic assessment, crop transportation, etc. Conventional classification methods, such as support vector machine (SVM) [3], K-nearest neighbor (KNN) [4], maximum likelihood classification (MLC) [5], etc., have been successfully applied in crop classification. It is still very important to develop new crop classification technologies

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Methods

Results

Discussion

Conclusion