The tradeoff among spatial, temporal, and spectral resolution of remote sensing (RS) images due to sensor properties limits the development of RS applications. Most image enhancement studies tend to focus on either spatio-temporal fusion or spatio-spectral fusion. As a more comprehensive solution, spatial–temporal-spectral fusion (STSF) is complicated but its potential is worth to be further explored. In this study, we propose a novel STSF method from the perspective of multi-temporal Pansharpening. Canny edge extraction is applied to Panchromatic (PAN) images to identify edges while avoiding the disruption of multi-temporal land cover changes. We then build a TemPanSharpening net (TPSnet) which only uses one high-spatio-low-spectra-temporal PAN and one low-spatio-high-spectra-temporal multispectral image as input. TPSnet follows a super-resolution structure and embeds two basic modules: residual-in-residual dense blocks (RRDB) and convolutional block attention module (CBAM). A series of interior ablation experiments were conducted on TPSnet and we also compared it with some representative spatio-temporal fusion, Pansharpening, and STSF algorithms. TPSnet presented satisfactory performance on complicated meter-level ground surfaces according to the quantitative evaluation result, and it demonstrated excellent robustness to land cover change.

中文翻译：

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TemPanSharpening：基于深度学习和边缘提取的多时相全色锐化解决方案

由于传感器特性，遥感 (RS) 图像的空间、时间和光谱分辨率之间的权衡限制了遥感应用的发展。大多数图像增强研究倾向于关注时空融合或时空融合。作为一种更全面的解决方案，时空谱融合（STSF）虽然复杂，但其潜力值得进一步探索。在本研究中，我们从多时相全色锐化的角度提出了一种新颖的 STSF 方法。 Canny 边缘提取应用于全色 (PAN) 图像以识别边缘，同时避免多时相土地覆盖变化的干扰。然后，我们构建了一个 TemPanSharpening 网络（TPSnet），它仅使用一张高时空低光谱 PAN 和一张低时空高光谱多光谱图像作为输入。 TPSnet遵循超分辨率结构，并嵌入两个基本模块：残差密集块（RRDB）和卷积块注意模块（CBAM）。在TPSnet上进行了一系列内部消融实验，我们还将其与一些代表性的时空融合、全色锐化和STSF算法进行了比较。根据定量评估结果，TPSnet在复杂的米级地表上表现出令人满意的性能，并且对土地覆盖变化表现出良好的鲁棒性。