  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.
