An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite

• URL: http://arxiv.org/abs/2308.13679v2
• Date: Sun, 3 Sep 2023 18:31:20 GMT
• Title: An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite
• Authors: Jon A. Justo, Joseph Garrett, Dennis D. Langer, Marie B. Henriksen, Radu T. Ionescu, and Tor A. Johansen
• Abstract summary: The HYPSO-1 Sea-Land-Cloud-Labeled dataset is an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission. 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. 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 in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://ntnu-smallsat-lab.github.io/hypso1_sea_land_clouds_dataset/ .

Related papers

• SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey [11.642711706384212]
  We introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images, 827K annotated segmentation masks, 696K detailed language descriptions.
  arXiv  Detail & Related papers  (2024-10-31T19:37:47Z)
• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [79.23689506129733]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• SSL4EO-L: Datasets and Foundation Models for Landsat Imagery [8.34029977985994]
  The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis. This paper introduces SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites.
  arXiv  Detail & Related papers  (2023-06-15T18:11:20Z)
• A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection [0.0]
  Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. We present a novel dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar.
  arXiv  Detail & Related papers  (2022-12-01T08:26:03Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• SpaceNet 6: Multi-Sensor All Weather Mapping Dataset [13.715388432549373]
  We present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge. MSAW covers 120 km2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels. We present a baseline and benchmark for building footprint extraction with SAR data and find that state-of-the-art segmentation models pre-trained on optical data, and then trained on SAR.
  arXiv  Detail & Related papers  (2020-04-14T13:43:11Z)
• Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset [1.0494061710470493]
  We explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Three DCNN models, including SegNet, U-Net, and LinkNet, are implemented next.
  arXiv  Detail & Related papers  (2020-03-30T17:32:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.