Journal Article

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

Sonobe, Rei

2019

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Agricultural land / Agricultural management / Algorithms / Artificial intelligence / Artificial neural networks / Beans / beets / Beta vulgaris / Classification / corn / crop / Crop identification / cropland / Crops / Cucurbita / Data mining / Decomposition / Deep learning / Dielectric properties / Dioscorea / Discriminant analysis / grasses / Kernels / Learning algorithms / Local government / Machine learning / Mathematical analysis / Multilayers / multiple kernel extreme learning machine (MKELM) / multiple kernel learning (MKL) / Neural networks / Parameter identification / polarimetric parameters / Polarimetry / Potatoes / radar vegetation index / Remote sensing / sample size / Satellites / Scattering / Sensitivity analysis / Solanum tuberosum / squashes / Studies / Sugar beets / Support vector machines / TerraSAR-X / Triticum / Vegetation / Wheat / Winter wheat / Yams / Zea mays