Determination of Relevant Hyperspectral Bands Using a Spectrally constrained CNN

Pestel-Schiller, Ulrike; Hu, Kai; Gritzner, Daniel; Östermann, Jörn

doi:10.1109/whispers52202.2021.9483986

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The network training strategy was the same as the simulation. As the number of hyperspectral bands up to 600, it is very efficient to start with a hyperspectral band reduction before the network training [44]. In order to contrast with the simulation datasets, we eliminated some low-brightness bands from the whole datasets, and then adopted the strategy of band superposition and average to select 31 bands from them.…”

Section: Resultsmentioning

confidence: 99%

Rapid Hyperspectral Imaging System via Sub-Sampling Coding

Bingchen

Zhao

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Resultsmentioning

confidence: 99%

Rapid Hyperspectral Imaging System via Sub-Sampling Coding

Bingchen

Zhao

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Close attention has been paid by researchers in [3,4] to the development of learning-based hyperspectral image compression methods, which have recently attracted much attention in the field of remote sensing. Such methods require the use of a large number of hyperspectral images during training to optimize all parameters and achieve high compression performance [5,6].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Sarinova,

Neftissov,

Rzayeva

et al. 2023

sjaitu

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This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth's surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate.

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