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.

为了解决多光谱遥感图像语义分割任务中存在的问题，本文首次提出了一种基于指标-空间-波状态叠加变换器（ISWSST）的方法，通过量子力学的启发，将指标、空间和波状态进行叠加或融合，以模拟量子叠加状态，并通过自适应投票决策来提高分类器的精度和分割准确性；设计了无损小波金字塔编码器-解码器模块，实现了图像的无损重构，并基于小波变换和逆小波变换模拟了量子纠缠，从而避免了边缘提取损失；提出了结合多光谱特征的方法，以准确提取原始分辨率图像中的地面物体，并引入量子力学原理解释了ISWSST的优越性。实验证明，ISWSST在多光谱遥感图像语义分割任务上优于现有的架构，有效提高了分割和边缘提取的准确性。

ISWSST: 多光谱遥感图像语义分割的索引-空间-波状态叠加变压器