On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

Abstract

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China

“…Polarimetric target decomposition methods are used commonly to extract various polarization features of land surface objects from PolSAR imagery. Depending on whether there is a change in the scattering properties of a target, polarimetric decomposition methods can be classified into two main categories: coherent decomposition that is based on a single-look Sinclair scattering matrix, and incoherent decomposition that is based on a multilook scattering matrix, i.e., the coherency matrix or the covariance matrix [ 23 , 24 ]. Usually, coherent decomposition is applied to analyze a “pure single target” that has deterministic or stationary scattering characteristics [ 24 ].…”

“…They acquired a classification map with overall accuracy of 87.5% based on the distinct scattering mechanisms of different land cover types. To improve classification accuracy, Xie et al [ 24 ] acquired a time series of 11 polarimetric RADARSAT-2 SAR images of an agricultural site in London, Ontario (Canada), and they employed the Neumann decomposition approach to extract the discriminant features and the random forest (RF) method to classify 9 land-cover types. The overall accuracy (OA) and the Kappa coefficient of the classification of the nine land-cover types in their study were 94.12% and 0.92, respectively.…”

Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

“…Polarimetric synthetic aperture radar (SAR) time series data can provide not only structural information but also continuous temporal changes of crops due to its capability of penetrating clouds and light rains. Therefore, multi-temporal polarimetric SAR data have been adopted for crop classifications [1][2][3][4]. The availability of various sources of satellite imagery enables to provide spatial, temporal, spectral and even structural features of land covers.…”

“…However, Sentinel-1 data have only two polarizations (VV + VH). When RADARSAT-2 full polarimetric SAR data were used in cropland classification, the polarimetric SAR parameters were usually extracted from the coherency matrix using different decomposition methods such as Pauli decomposition, Cloude-Pottier decomposition, Freeman-Durden decomposition [29], Neumann decomposition [3], and the optimum power [30] for crop classification. It has been studied that the elements of the coherency matrix of the fully polarimetric SAR data also show good performance in crop classification since the coherency matrix is the basic matrix representing the information of the polarimetric SAR data [15].…”

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

“…Polarimetric SAR (PolSAR), in addition to the characteristics mentioned above, is able to use the backscattering of polarization waves from objects to form images. In recent years, research on PolSAR has attracted wide interest in the field of remote sensing, and applications of PolSAR images have gradually increased, such as crop classification [1], ship detection [2], and change detection [3]. PolSAR image classification assigns a specific ground category to each pixel.…”