CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning

• URL: http://arxiv.org/abs/2201.08001v1
• Date: Thu, 20 Jan 2022 04:59:05 GMT
• Title: CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning
• Authors: Suhas Kotha, Anirudh Koul, Siddha Ganju, and Meher Kasam
• Abstract summary: We establish CELESTIAL-a self-supervised learning pipeline for effectively leveraging sparsely-labeled satellite imagery. This pipeline successfully adapts SimCLR, an algorithm that first learns image representations on unlabelled data and then fine-tunes this knowledge on the provided labels. Our results show CELESTIAL requires only a third of the labels that the supervised method needs to attain the same accuracy on an experimental dataset.
• Score: 0.34998703934432673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A common class of problems in remote sensing is scene classification, a fundamentally important task for natural hazards identification, geographic image retrieval, and environment monitoring. Recent developments in this field rely label-dependent supervised learning techniques which is antithetical to the 35 petabytes of unlabelled satellite imagery in NASA GIBS. To solve this problem, we establish CELESTIAL-a self-supervised learning pipeline for effectively leveraging sparsely-labeled satellite imagery. This pipeline successfully adapts SimCLR, an algorithm that first learns image representations on unlabelled data and then fine-tunes this knowledge on the provided labels. Our results show CELESTIAL requires only a third of the labels that the supervised method needs to attain the same accuracy on an experimental dataset. The first unsupervised tier can enable applications such as reverse image search for NASA Worldview (i.e. searching similar atmospheric phenomenon over years of unlabelled data with minimal samples) and the second supervised tier can lower the necessity of expensive data annotation significantly. In the future, we hope we can generalize the CELESTIAL pipeline to other data types, algorithms, and applications.

Related papers

• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning [0.30458514384586394]
  We propose a novel approach based on well-established self-supervised learning, clustering, and manifold learning techniques. We test our approach using several publicly available datasets, namely CIFAR10, Imagenette, DeepWeeds, and EuroSAT. We obtain superior performance for the datasets considered with a much simpler approach compared to other methods in the literature.
  arXiv  Detail & Related papers  (2023-05-17T09:17:59Z)
• Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence [3.3810628880631226]
  This work presents a novel methodology to automate the creation of datasets for the detection of target events. The presented approach first processes the raw data by applying a pipeline consisting of spatial band registration and georeferencing. It detects the target events by leveraging event-specific state-of-the-art algorithms on the Level-1C products. We apply the proposed methodology to realize THRawS (Thermal Hotspots in Raw Sentinel-2 data), the first dataset of Sentinel-2 raw data containing warm thermal hotspots.
  arXiv  Detail & Related papers  (2023-05-12T09:54:21Z)
• Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels [1.534667887016089]
  This work proposes a hybrid unsupervised and supervised learning method to pre-train models applied in Earth observation downstream tasks. We combine a contrastive approach to pre-train models with a pixel-wise regression pre-text task to predict coarse elevation maps.
  arXiv  Detail & Related papers  (2023-04-13T23:01:11Z)
• Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection [0.0]
  We study in-domain self-supervised representation learning for object detection on very high resolution optical satellite imagery. We use the large land use classification dataset Functional Map of the World to pretrain representations with an extension of the Momentum Contrast framework. We then investigate this model's transferability on a real-world task of fine-grained vehicle detection and classification on Preligens proprietary data.
  arXiv  Detail & Related papers  (2022-10-21T08:41:22Z)
• 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)
• AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [69.47585818994959]
  We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. We utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas.
  arXiv  Detail & Related papers  (2022-01-31T20:02:22Z)
• Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images [1.0152838128195467]
  Semi-supervised techniques generate pseudo-labels from a small set of labeled examples. We propose to use an active learning-based sampling strategy to select a highly representative set of labeled training data. We report a 27% improvement in mIoU with as little as 2% labeled data using active learning sampling strategies.
  arXiv  Detail & Related papers  (2021-10-15T00:29:31Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Streaming Self-Training via Domain-Agnostic Unlabeled Images [62.57647373581592]
  We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models. Key to SST are two crucial observations: (1) domain-agnostic unlabeled images enable us to learn better models with a few labeled examples without any additional knowledge or supervision; and (2) learning is a continuous process and can be done by constructing a schedule of learning updates.
  arXiv  Detail & Related papers  (2021-04-07T17:58:39Z)
• Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data [64.40187171234838]
  Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of re-mote sensing representations. SeCo will be made public to facilitate transfer learning and enable rapid progress in re-mote sensing applications.
  arXiv  Detail & Related papers  (2021-03-30T18:26:39Z)

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.