# Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework

## TL;DR

This paper introduces a novel multi-task deep learning framework that utilizes amplitude and phase of complex-valued PolSAR data as input, significantly improving classification accuracy by tailoring CNN architectures to the data's properties.

## Contribution

It develops a specialized CNN architecture for PolSAR data that leverages amplitude and phase information, avoiding complex-valued operations and enhancing classification performance.

## Key findings

- Amplitude and phase inputs improve classification accuracy.
- The proposed architecture adapts well to different PolSAR datasets.
- Depthwise separable convolution enhances phase information extraction.

## Abstract

Convolutional neural networks (CNNs) have been widely used to improve the accuracy of polarimetric synthetic aperture radar (PolSAR) image classification. However, in most studies, the difference between PolSAR images and optical images is rarely considered. Most of the existing CNNs are not tailored for the task of PolSAR image classification, in which complex-valued PolSAR data have been simply equated to real-valued data to fit the optical image processing architectures and avoid complex-valued operations. This is one of the reasons CNNs unable to perform their full capabilities in PolSAR classification. To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form. In this paper, considering the properties of complex-valued numbers, amplitude and phase of complex-valued PolSAR data are extracted as the input for the first time to maintain the integrity of original information while avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN) architecture is proposed to match the improved input form and achieve better classification results. Furthermore, depthwise separable convolution is introduced to the proposed architecture in order to better extract information from the phase information. Experiments on three PolSAR benchmark datasets not only prove that using amplitude and phase as the input do contribute to the improvement of PolSAR classification, but also verify the adaptability between the improved input form and the well-designed architectures.