Paper Info
Title
An Adversarial Generative Network For Crop Classification From Remote Sensing Timeseries Images
Abstract
Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN's discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.
Year
DOI
Venue
2021
10.3390/rs13010065
REMOTE SENSING
Keywords
DocType
Volume
adversarial generative network, crop classification, deep learning, multispectral remote sensing
Journal
13
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jingtao Li100.68
Yonglin Shen201.01
Chao Yang301.01