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Multi-sensor multispectral reconstruction framework based on projection and reconstruction
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AI
DOI：10.1007/s11432-023-3910-y.png)
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Abstract
En 中文
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 multisensor 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.
Keywords:
spectral reconstruction
spectral superresolution
hyperspectral image
multispectral image
neural networks
multi-sensor
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