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

Abstract

Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D.

Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

“…The crop structural features were also well characterized by their unique scattering patterns using parameters derived from polarimetric decompositions (i.e., FD, CP, and ND). The difference in temporal evolution patterns of scattering mechanisms among crops provided useful information for crop classification, which have been frequently used in crop growth monitoring and classification in previous reports [7,17,27,38,39,41]. Among the polarimetric complex correlation parameters, the correlation coefficient ρ HHVV showed the highest sensitivity to crop growth, which has been proven useful for identification of growth stages [31,36].…”

“…The results using single groups of polarimetric observables showed that polarimetric decompositions (ND, FD, and CP), backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with OAs greater than 87%. This is because these polarimetric observables were found to be very sensitive to crop structure and growth parameters [7,17,27,38,39,41]. In previous studies, the common way for constructing the feature set was by stacking all polarimetric observables from various sources [7,17,27,39].…”

“…This is because these polarimetric observables were found to be very sensitive to crop structure and growth parameters [7,17,27,38,39,41]. In previous studies, the common way for constructing the feature set was by stacking all polarimetric observables from various sources [7,17,27,39]. However, in our case, the classifications using an optimal combination of polarimetric observables with reduced number of polarimetric observables, i.e., eight groups (NM, FD, Ra, Pha, Ro, CP, RVI and Pauli), produced better overall accuracies for crop classification.…”

“…These dynamics cause challenges for single-date SAR applications, but provide unique information that may be helpful to improve results [5]. As a consequence, SAR time-series data are increasingly used for crop growth monitoring and crop type classification [17][18][19][20][21][22][23][24][25][26][27].…”

“…Few efforts have been made in optimal combination of SAR features for crop classification. The common way for constructing a feature set is to stack all polarimetric observables from various sources [7,17,27,39]. In addition, comparable or even better crop classification results might be obtained by combining fewer images on dates of critical phenology [10,27].…”