Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery

• URL: http://arxiv.org/abs/2212.13613v1
• Date: Tue, 27 Dec 2022 20:56:34 GMT
• Title: Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery
• Authors: Joachim Moortgat, Ziwei Li, Michael Durand, Ian Howat, Bidhyananda Yadav, Chunli Dai
• Abstract summary: This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. We use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available.
• Score: 2.121978045345352
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.

Related papers

• Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data [0.0]
  This dissertation explores several novel approaches based on stereo and multi-view satellite image-derived 3D geospatial data. It introduces four parts of novel approaches that deal with the spatial and temporal challenges with satellite-derived 3D data. Overall, this dissertation demonstrates the extensive potential of satellite photogrammetry applications in addressing urban and environmental challenges.
  arXiv  Detail & Related papers  (2024-04-18T20:02:52Z)
• 3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching [6.13702551312774]
  We introduce a large-scale Multi-sources,Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching (3MOS) It consists of 155K optical-SAR image pairs, including SAR data from six commercial satellites, with resolutions ranging from 1.25m to 12.5m. The data has been classified into eight scenes including urban, rural, plains, hills, mountains, water, desert, and frozen earth.
  arXiv  Detail & Related papers  (2024-04-01T00:31:11Z)
• DiffusionSat: A Generative Foundation Model for Satellite Imagery [63.2807119794691]
  We present DiffusionSat, to date the largest generative foundation model trained on a collection of publicly available large, high-resolution remote sensing datasets. Our method produces realistic samples and can be used to solve multiple generative tasks including temporal generation, superresolution given multi-spectral inputs and in-painting.
  arXiv  Detail & Related papers  (2023-12-06T16:53:17Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Towards Transformer-based Homogenization of Satellite Imagery for Landsat-8 and Sentinel-2 [1.4699455652461728]
  Landsat-8 (NASA) and Sentinel-2 (ESA) are two prominent multi-spectral imaging satellite projects that provide publicly available data. This work provides a first glance at the possibility of using a transformer-based model to reduce the spectral and spatial differences between observations from both satellite projects.
  arXiv  Detail & Related papers  (2022-10-14T09:13:34Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• Learning a Sensor-invariant Embedding of Satellite Data: A Case Study for Lake Ice Monitoring [19.72060218456938]
  We learn a joint, sensor-invariant embedding within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. By fusing satellite data, we map lake ice at a temporal resolution of 1.5 days.
  arXiv  Detail & Related papers  (2021-07-19T18:11:55Z)
• Big Plastic Masses Detection using Sentinel 2 Images [91.3755431537592]
  This communication describes a preliminary research on detection of big masses of plastic (marine litter) on the oceans and seas using EO (Earth Observation) satellite systems.
  arXiv  Detail & Related papers  (2021-03-17T10:45:33Z)
• Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach [10.72081512622396]
  This paper evaluates the proposed geographical hidden Markov tree model through case studies on high-resolution aerial imagery. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms.
  arXiv  Detail & Related papers  (2020-08-25T18:35:28Z)
• Deep learning for lithological classification of carbonate rock micro-CT images [52.77024349608834]
  This work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images. Four convolutional neural network models were proposed. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second.
  arXiv  Detail & Related papers  (2020-07-30T19:14:00Z)
• Unlimited Resolution Image Generation with R2D2-GANs [69.90258455164513]
  We present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission. The data produced is continuous, realistically-looking, and can also be generated at least two times faster than the real speed of acquisition.
  arXiv  Detail & Related papers  (2020-03-02T17:49:32Z)

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.