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Oct 24, 2023 · In this work, we train and test 20 models for multi-class segmentation in hyperspectral imagery, selected for their potential in future space deployment.
We consider how to prioritize HS image downlink based on sea, land, and cloud coverage levels from the segmented images. We comparatively evaluate the models ...
In this context, the use of deep learning (DL) techniques for segmentation in hyperspectral (HS) satellite imagery offers advantages for remote sensing ...
This GitHub repository serves as the supplementary material for the paper titled Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep ...
Dec 1, 2024 · PDF | Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference.
The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and ...
In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available ...
The HYPSO-1 Sea-Land-Cloud-Labeled Dataset is an open-source dataset that contains 200 diverse hyperspectral images captured by the HYPSO-1 satellite mission.
Nov 4, 2024 · We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories.
Aug 24, 2023 · In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 ...