首页|Multi-sensor multispectral reconstruction framework based on projection and reconstruction

Multi-sensor multispectral reconstruction framework based on projection and reconstruction

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The scarcity and low spatial resolution of hyperspectral images(HSIs)have become a major problem limiting the application of the images.In recent years,spectral reconstruction(SR)has been applied to convert multispectral images(MSIs)with abundant quantities and high spatial resolution into HSIs.With the launch of several new multispectral(MS)satellites with a short repeat period,the simultaneous acquisition of images from multiple MS sensors in the same area is gradually becoming feasible.Unfortunately,existing SR methods only consider the reconstruction of the MSIs of a single sensor without considering using MSIs from different MS sensors to obtain a better construction effect through their complementary bands.However,multi-sensor SR is characterized by two problems:inconsistency in the amplitude information of real multi-sensor imaging and difficulty in the extraction of the complex correlations of bands from different sensors.To solve these problems,this paper proposes a multi-sensor SR framework based on a two-step approach in which the problems of amplitude inconsistency and band information extraction are solved using an ideal projection network and an ideal multi-sensor SR network,respectively.The effectiveness of the proposed method is verified by experiments on three datasets.

spectral reconstructionspectral superresolutionhyperspectral imagemultispectral imageneural networksmulti-sensor

Tianshuai LI、Tianzhu LIU、Xian LI、Yanfeng GU、Yukun WANG、Yushi CHEN

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School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China

Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing,Harbin 150001,China

国家杰出青年科学基金National Natural Science Foundation of Key International Cooperation

6202510761720106002

2024

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子:0.715
ISSN:1674-733X
年,卷(期):2024.67(3)
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