Crop classification based on multi-temporal PolSAR images with a single tensor network
Pattern Recognition(2023)
Abstract
Accurate and reliable discrimination of crop categories is a significant data source for agricultural moni-toring 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 Pol-SAR 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 incorpo-rated 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 classifica-tion 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 trans-formation 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. & COPY; 2023 Elsevier Ltd. All rights reserved.
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Key words
Polarimetric synthetic aperture radar,(PolSAR),Crop classification,Tensor affine transformation network,(TATN)
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