ISWSST：用于多光谱遥感影像语义分割的索引-空间-波态叠加Transformer
ISWSST: Index-space-wave State Superposition Transformers for Multispectral Remotely Sensed Imagery Semantic Segmentation
AI总结：
本文首次提出受量子力学启发的ISWSST模型，通过索引-空间-波态叠加、无损小波编解码和多光谱特征融合，提升多光谱遥感影像语义分割与边缘提取精度。
摘要：
目前，多光谱遥感影像（MSRSI）的语义分割任务面临以下问题：1）通常只考虑单一域特征（即空间域或频率域）；2）编码器中的下采样操作通常会导致边缘提取精度损失；3）MSRSI的多通道特征未被充分考虑；4）遥感先验知识未被充分利用。为解决上述问题，受量子力学启发，首次提出了一种索引-空间-波态叠加Transformer（ISWSST）用于MSRSI语义分割，其优势如下：1）通过自适应投票决策（即集成学习思想）将索引态、空间态和波态进行叠加或融合，以模拟量子叠加，从而成为更强的分类器并提高分割精度；2）设计了一种无损小波金字塔编码器-解码器模块，基于小波变换和逆小波变换无损重建图像并模拟量子纠缠，以避免边缘提取损失；3）提出结合多光谱特征（即遥感索引和通道注意力机制），从原始分辨率图像中准确提取地物；4）引入量子力学来解释ISWSST的潜在优越性。实验表明，ISWSST在MSRSI分割任务中得到了验证，并优于最先进的架构，有效提高了分割和边缘提取精度。代码将在论文被接收后公开提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
