A Full Tensor Decomposition Network for Crop Classification with Polarization Extension

Abstract

The multisource data fusion technique has been proven to perform better in crop classification. However, traditional fusion methods simply stack the original source data and their corresponding features, which can be only regarded as a superficial fusion method rather than deep fusion. This paper proposes a pixel-level fusion method for multispectral data and dual polarimetric synthetic aperture radar (PolSAR) data based on the polarization extension, which yields synthetic quad PolSAR data. Then we can generate high-dimensional features by means of various polarization decomposition schemes. High-dimensional features usually cause the curse of the dimensionality problem. To overcome this drawback in crop classification using the end-to-end network, we propose a simple network, namely the full tensor decomposition network (FTDN), where the feature extraction in the hidden layer is accomplished by tensor transformation. The number of parameters of the FTDN is considerably fewer than that of traditional neural networks. Moreover, the FTDN admits higher classification accuracy by making full use of structural information of PolSAR data. The experimental results demonstrate the effectiveness of the fusion method and the FTDN model.

MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

“…Zhou et al [4] extracted a six-dimensional real-valued feature vector from the polarization covariance matrix and then fed the six-channel real images into a deep network to learn hierarchical polarimetric spatial features, achieving satisfactory results in classifying 15 terrains in the Flevoland data. Zhang et al [5] employed polarization decomposition to crops in PolSAR scenes and then fed the resulting polarization tensors into a tensor decomposition network for dimension reduction, which achieved better classification accuracy. However, these pixel-scale terrain classification methods cannot be directly applied to image-scale target recognition tasks.…”

Synthetic Aperture Radar Remote Sensing for Crop Classification

“…High resolution images have advanced discrimination capabilities, but obtaining time series datasets that cover the entire growth cycles of crops is challenging [3][4][5][6][7]. This is due to the dependence of optical satellite image acquisition on atmospheric conditions, which often results in contaminated and cloud masked images, particularly in Kharif crops and fruit trees [12][13][14][15][16][26][27][28][29]. Synthetic Aperture Radar data sets offer an advantage in this regard.…”