Next Article in Journal
Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators
Next Article in Special Issue
A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering
Previous Article in Journal
Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
Previous Article in Special Issue
Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks
Open AccessArticle

A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images

Department of Engineering-Signal Processing, Faculty of Science and Technology, Aarhus University, 8200 Aarhus N, Denmark
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 990; https://doi.org/10.3390/rs11080990
Received: 25 March 2019 / Revised: 14 April 2019 / Accepted: 17 April 2019 / Published: 25 April 2019
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
  |  
PDF [4844 KB, uploaded 28 April 2019]
  |     |  

Abstract

In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. Radar sensors are capable of imaging Earth’s surface independently of the weather conditions, local time of day, penetrating of waves through clouds, and containing spatial information on agricultural crop types. Based on these characteristics, the main goal sought in this research is to reveal the SAR imaging data capability in recognizing various agricultural crops in the main growth season in a more clarified and detailed way by using a deep-learning-based method. In the present research, the multi-temporal C-band Sentinel 1 images were used to classify 14 major classes of agricultural crops plus background in Denmark. By considering the capability of a deep learning method in analyzing satellite images, a novel, optimal, and lightweight network structure was developed and implemented based on a combination of a fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM) network. The average pixel-based accuracy and Intersection over Union obtained from the proposed network were 86% and 0.64, respectively. Winter rapeseed, winter barley, winter wheat, spring barley, and sugar beet had the highest pixel-based accuracies of 95%, 94%, 93%, 90%, and 90%; respectively. The pixel-based accuracies for eight crop types and the background class were more than 84%. The network prediction showed that in field borders the classification confidence was lower than the center regions of the fields. However, the proposed structure has been able to identify different crops in multi-temporal Sentinel 1 data of a large area of around 254 thousand hectares with high performance. View Full-Text
Keywords: long-short term memory; fully convolutional network; sequential network; multi-temporal data; crop classification; Sentinel 1 long-short term memory; fully convolutional network; sequential network; multi-temporal data; crop classification; Sentinel 1
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Teimouri, N.; Dyrmann, M.; Jørgensen, R.N. A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sens. 2019, 11, 990.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top