The despeckling task for synthetic aperture radar (SAR) has long faced the challenge of obtaining clean images. Although unsupervised deep learning despeckling methods alleviate this issue, they often struggle to balance despeckling effectiveness and the preservation of spatial details. Furthermore, some unsupervised despeckling approaches overlook the effect of land cover changes when dual-temporal SAR images are used as training data. To address this issue, we propose a multitask learning framework for dual-polarization SAR imagery despeckling and change detection (MTDN). This framework integrates polarization decomposition mechanisms with dual-polarization SAR images, and utilizes a change detection network to guide and constrain the despeckling network for optimized performance. Specifically, the despeckling branch of this framework incorporates polarization and spatiotemporal information from dual-temporal dual-polarization SAR images to construct a despeckling network. It employs various attention mechanisms to recalibrate features across local/global, channel, and spatial dimensions, and before and after despeckling. The change detection branch, which combines Transformer and convolutional neural networks, helps the despeckling branch effectively filter out spatiotemporal information with substantial changes. The multitask joint loss function is weighted by the generated change detection mask to achieve collaborative optimization. Despeckling and change detection experiments are conducted using a dual-polarization SAR dataset to assess the effectiveness of the proposed framework. The despeckling experiments indicate that MTDN efficiently eliminates speckle noise while preserving polarization information and spatial details, and surpasses current leading SAR despeckling methods. The equivalent number of looks (ENL) for MTDN in the agricultural change area increased to 155.0630, and the edge detail preservation (EPD) metric improved to 0.9963, which is better than the comparison methods. Furthermore, the change detection experiments confirm that MTDN yields precise predictions, highlighting its exceptional capability in practical applications. The code, dataset, and pre-trained MTDN will be available at https://github.com/WHU-SGG-RS-Pro-Group/PolSAR-DESPECKLING-MTDN for verification.

中文翻译：

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一种用于时间变化检测场景下双极化 SAR 图像去斑的多任务学习框架


合成孔径雷达 （SAR） 的去斑任务长期以来一直面临着获得清晰图像的挑战。尽管无监督深度学习去斑方法缓解了这个问题，但它们通常难以平衡去斑效果和空间细节的保留。此外，当双时间 SAR 图像用作训练数据时，一些无监督的去斑方法忽略了土地覆被变化的影响。为了解决这个问题，我们提出了一种用于双极化 SAR 图像去斑和变化检测 （MTDN） 的多任务学习框架。该框架将偏振分解机制与双偏振 SAR 图像集成在一起，并利用变化检测网络来引导和约束去斑网络以实现最佳性能。具体来说，该框架的去斑分支结合了来自双时相双极化 SAR 图像的极化和时空信息来构建去斑网络。它采用各种注意力机制来重新校准局部/全局、通道和空间维度以及去斑之前和之后的特征。变化检测分支结合了 Transformer 和卷积神经网络，帮助 despeckling 分支有效地过滤掉具有大量变化的时空信息。多任务联合损失函数通过生成的变化检测掩码进行加权，实现协同优化。使用双极化 SAR 数据集进行去斑和变化检测实验，以评估所提框架的有效性。 去斑实验表明，MTDN 在保留极化信息和空间细节的同时有效地消除了散斑噪声，并超越了目前领先的 SAR 去斑方法。农业变化区 MTDN 的等效查看次数 （ENL） 增加到 155.0630，边缘细节保留 （EPD） 指标提高到 0.9963，优于比较方法。此外，变化检测实验证实 MTDN 可以产生精确的预测，凸显了其在实际应用中的卓越能力。代码、数据集和预训练的 MTDN 将在 https://github.com/WHU-SGG-RS-Pro-Group/PolSAR-DESPECKLING-MTDN 上提供以供验证。