Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 14; p. 2749
• Main Authors: Zhang, Wei-Tao, Wang, Min, Guo, Jiao, Lou, Shun-Tian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 13.07.2021
• Subjects: Accuracy; Agriculture; Algorithms; Artificial neural networks; Classification; Classifiers; Coders; crop classification; crop discrimination network with multi-scale features (MSCDN); Crops; Data compression; Decomposition; Food security; Neural networks; polarimetric synthetic aperture radar (PolSAR); polarimetry; Principal components analysis; Radar polarimetry; Remote sensing; Satellites; sparse auto-encoder (AE); Support vector machines; Synthetic aperture radar
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13142749

Accurate and reliable crop classification information is a significant data source for agricultural monitoring and food security evaluation research. It is well-known that polarimetric synthetic aperture radar (PolSAR) data provides ample information for crop classification. Moreover, multi-temporal PolSAR data can further increase classification accuracies since the crops show different external forms as they grow up. In this paper, we distinguish the crop types with multi-temporal PolSAR data. First, due to the “dimension disaster” of multi-temporal PolSAR data caused by excessive scattering parameters, a neural network of sparse auto-encoder with non-negativity constraint (NC-SAE) was employed to compress the data, yielding efficient features for accurate classification. Second, a novel crop discrimination network with multi-scale features (MSCDN) was constructed to improve the classification performance, which is proved to be superior to the popular classifiers of convolutional neural networks (CNN) and support vector machine (SVM). The performances of the proposed method were evaluated and compared with the traditional methods by using simulated Sentinel-1 data provided by European Space Agency (ESA). For the final classification results of the proposed method, its overall accuracy and kappa coefficient reaches 99.33% and 99.19%, respectively, which were almost 5% and 6% higher than the CNN method. The classification results indicate that the proposed methodology is promising for practical use in agricultural applications.