Abstract
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.
Highlights
9-dimensional compressed features were derived from the original 252-dimensional multi-temporal features using various methods, namely locally linear embedded (LLE), principle component analysis (PCA), stacked sparse auto-encoder (S-SAE) and the proposed NC-SAE
We proposed a novel classification method, namely MSCDN, for multitemporal polarimetric synthetic aperture radar (PolSAR) data classification
To solve the problem of the dimension disaster, firstly, we constructed a sparse auto-encoder with non-negativity constraints (NC-SAE) which has an improved sparsity to reduce the data dimension of scattering features extracted from multi-temporal PolSAR images
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Crop classification plays an important role in remote sensing monitoring of agricultural conditions, and it is a premise for further monitoring of crop growth and yields [1,2]. Areas and space distribution information of crops have been acquired in a timely and accurate manner, it can provide scientific evidence of reasonable adjustment for agriculture structure. Crop classification has great significance for guidance of agriculture production, rational distribution of farming resources and guarantee of national food security [3,4,5]
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