Next Article in Journal
Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies
Previous Article in Journal
A New GIS-Based Model for Karst Dolines Mapping Using LiDAR; Application of a Multidepth Threshold Approach in the Yucatan Karst, Mexico
Previous Article in Special Issue
On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data
Article Menu

Export Article

Open AccessArticle

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
Remote Sens. 2019, 11(10), 1148; https://doi.org/10.3390/rs11101148
Received: 17 April 2019 / Revised: 7 May 2019 / Accepted: 11 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)
  |  
PDF [3666 KB, uploaded 14 May 2019]
  |  

Abstract

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields. View Full-Text
Keywords: crop; multiple kernel extreme learning machine (MKELM); multiple kernel learning (MKL); polarimetric parameters; radar vegetation index; TerraSAR-X crop; multiple kernel extreme learning machine (MKELM); multiple kernel learning (MKL); polarimetric parameters; radar vegetation index; TerraSAR-X
Figures

Figure 1

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

Sonobe, R. Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data. Remote Sens. 2019, 11, 1148.

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