Nov 6, 2023 · To illustrate the efficacy of the masking procedure, the proposed method is deployed on three challenging applications scenarios that demand ...
Nov 7, 2023 · Abstract. The presence of undesired background areas associated with potential noise and unknown spectral characteristics degrades the ...
Mar 13, 2024 · The first project is a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a maize field across multiple diurnal cycles ...
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Oct 31, 2023 · Our system tries to constrain to information found in this paper. Results quality may vary. Learn more about how we generate these answers.
A: We propose the use of masked image modeling to pre- train spatial-spectral transformer networks on a large dataset of unlabeled hyperspectral EnMAP data. B: ...
Dec 31, 2021 · Existing deep HSI reconstruction models are generally trained on paired data ... Our code and pre-trained model are available at this https URL ...
Masking Hyperspectral Imaging Data with Pretrained Models · 1 code ... Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection.
This repository contains the source code, pre-trained models and benchmark testing data for the ECCV2022 Oral paper Modeling mask uncertainty in hyperspectral ...
Apr 4, 2023 · SpectralMAE models the spectral image reconstruction problem as predicting masked patches from the visible ones. ... Hyperspectral Image Data ...
Image masking, Radial basis function, Gamma (γ): 1 ... Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data.