Crop classification based on multi-temporal PolSAR images with a single tensor network.

• High-dimensional tensor sample formed by crop PolSAR data contains rich structural information. • The "dimension reduction" and "vectorized learning" seriously damaged the structural information. • A novel tensor affine transform neural network (TATN) architecture is built for crop classification. • The proposed TATN model can be trained by error back-propagation strategy. • TATN achieved an accuracy gain of 8.05%, and the number of parameters is only 12.98% of CNN model. Accurate and reliable discrimination of crop categories is a significant data source for agricultural monitoring and food security evaluation research. The convolutional neural network (CNN) model is one of the most popular classifiers for crop discrimination based on polarimetric synthetic aperture radar (PolSAR) data. However, it is better to avoid directly using large amounts of raw features that extracted from PolSAR data for CNN models because of the "dimension disaster" problem caused by multiple periods and various feature extraction schemes. Consequently, an extra feature compression model has to be incorporated to mitigate the "dimension disaster" problem. However, ill coupling of the two models may result in degraded classification performance, thus the combination of two models has to be further optimized. In this paper, we propose a novel single tensor affine transformation network (TATN) for crop classification using multi-temporal PolSAR data, where the input sample of the network is a higher order tensor formed by raw features, and the hidden layers of the network adopt the tensor affine transformation rather than convolution to extract discriminative features for classification. Since the tensor affine transformation preserves the structural information of the original input tensor samples, the TATN is expected to achieve a higher crop classification accuracy. Moreover, the TATN holds less amount of parameters than most of deep learning models, which enables to avoid the extra feature compression procedure. The experimental results validate the merits of our model. [ABSTRACT FROM AUTHOR]