Remotely sensed imagery interpretation (RSII) faces the three major problems: (1) objective representation of spatial distribution patterns; (2) edge uncertainty problem caused by downsampling encoder and intrinsic edge noises (e.g., mixed pixel and edge occlusion etc.); and (3) false detection problem caused by geometric registration error in change detection. To solve the aforementioned problems, uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed, whose superiorities are as follows: (1) a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) is proposed to enhance the interaction of spatially frequency-wise stationary and non-stationary features to yield high-fidelity edge extraction result. Inspired by HRFormer, SHCP proposes high-frequency-wise stream to replace high-resolution-wise stream in HRFormer through the whole encoder-decoder process with parallel frequency-wise high-to-low streams, so it improves the edge extraction accuracy by continuously remaining high-frequency information; (2) a mask-and-geo-knowledge-based uncertainty diffusion module (MUDM), which is a self-supervised learning strategy, is proposed to improve the edge accuracy of extraction and change detection by gradually removing the simulated spectrum noises based on geo-knowledge and the generated diffused spectrum noises; (3) a frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection. Besides the aforementioned spectrum noises in semantic segmentation, MUDM is also a self-supervised learning strategy to effectively reduce the edge false change detection from the generated imagery with geometric registration error.

翻译：遥感影像解译面临三大主要问题：(1) 空间分布模式的客观表征；(2) 由下采样编码器及固有边缘噪声（如混合像元、边缘遮挡等）引起的边缘不确定性问题；(3) 变化检测中由几何配准误差导致的误检问题。为解决上述问题，本文首次提出了基于不确定性扩散模型的高频Transformer网络。其优势如下：(1) 提出了空间平稳与非平稳高频连接范式，通过增强空间频率维度上平稳与非平稳特征的交互，以产生高保真的边缘提取结果。受HRFormer启发，SHCP提出高频流，以取代HRFormer中的高分辨率流，贯穿整个具有并行频率维度高到低流的编码器-解码器过程，从而通过持续保留高频信息来提高边缘提取精度；(2) 提出了一种基于掩码与地理知识的无监督不确定性扩散模块，这是一种自监督学习策略，通过基于地理知识逐步去除模拟的光谱噪声及生成的扩散光谱噪声，以提高边缘提取和变化检测的精度；(3) 首次提出了频率维度半伪孪生UDHF2-Net，以在变化检测中平衡精度与复杂度。除语义分割中的上述光谱噪声外，MUDM也是一种自监督学习策略，能有效减少由存在几何配准误差的生成影像引起的边缘误变化检测。