Currently the semantic segmentation task of multispectral remotely sensed imagery (MSRSI) faces the following problems: 1) Usually, only single domain feature (i.e., space domain or frequency domain) is considered; 2) downsampling operation in encoder generally leads to the accuracy loss of edge extraction; 3) multichannel features of MSRSI are not fully considered; and 4) prior knowledge of remote sensing is not fully utilized. To solve the aforementioned issues, an index-space-wave state superposition Transformer (ISWSST) is the first to be proposed for MSRSI semantic segmentation by the inspiration from quantum mechanics, whose superiority is as follows: 1) index, space and wave states are superposed or fused to simulate quantum superposition by adaptively voting decision (i.e., ensemble learning idea) for being a stronger classifier and improving the segmentation accuracy; 2) a lossless wavelet pyramid encoder-decoder module is designed to losslessly reconstruct image and simulate quantum entanglement based on wavelet transform and inverse wavelet transform for avoiding the edge extraction loss; 3) combining multispectral features (i.e. remote sensing index and channel attention mechanism) is proposed to accurately extract ground objects from original resolution images; and 4) quantum mechanics are introduced to interpret the underlying superiority of ISWSST. Experiments show that ISWSST is validated and superior to the state-of-the-art architectures for the MSRSI segmentation task, which improves the segmentation and edge extraction accuracy effectively. Codes will be available publicly after our paper is accepted.

翻译：当前多光谱遥感影像语义分割任务面临以下问题：1)通常仅考虑单一域特征（即空间域或频域）；2)编码器中的下采样操作通常导致边缘提取精度损失；3)未充分考虑多光谱影像的多通道特征；4)未充分利用遥感先验知识。针对上述问题，受量子力学启发首次提出索引-空间-波态叠加Transformer用于多光谱遥感影像语义分割，其优势在于：1)通过自适应投票决策（即集成学习思想）叠加融合索引态、空间态与波态以模拟量子叠加效应，构建更强分类器并提升分割精度；2)设计无损小波金字塔编解码模块，基于小波变换与逆变换实现图像无损重建并模拟量子纠缠，避免边缘提取损失；3)融合多光谱特征（即遥感指数与通道注意力机制），实现原始分辨率影像地物的精准提取；4)引入量子力学原理解释ISWSST的底层优越性。实验表明ISWSST在多光谱遥感影像分割任务中验证有效且优于现有先进架构，显著提升了分割与边缘提取精度。代码将在论文录用后公开。