638 research outputs found
Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches
VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts
Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9
Workshops Stream 1 10
Workshop Stream 2 11
Workshop Stream 3 12
Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14
Session 2 – Visualisation, communication & Teaching 27
Session 3 – Applying Machine Learning in Geosciences 36
Session 4 – Digital Outcrop Characterisation & Analysis 49
Session 5 – Airborne & Remote Mapping 58
Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69
Session 7 – Applications in Hydrology & Ecology 82
Poster Contributions 9
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
The past years have witnessed great progress on remote sensing (RS) image
interpretation and its wide applications. With RS images becoming more
accessible than ever before, there is an increasing demand for the automatic
interpretation of these images. In this context, the benchmark datasets serve
as essential prerequisites for developing and testing intelligent
interpretation algorithms. After reviewing existing benchmark datasets in the
research community of RS image interpretation, this article discusses the
problem of how to efficiently prepare a suitable benchmark dataset for RS image
interpretation. Specifically, we first analyze the current challenges of
developing intelligent algorithms for RS image interpretation with bibliometric
investigations. We then present the general guidances on creating benchmark
datasets in efficient manners. Following the presented guidances, we also
provide an example on building RS image dataset, i.e., Million-AID, a new
large-scale benchmark dataset containing a million instances for RS image scene
classification. Several challenges and perspectives in RS image annotation are
finally discussed to facilitate the research in benchmark dataset construction.
We do hope this paper will provide the RS community an overall perspective on
constructing large-scale and practical image datasets for further research,
especially data-driven ones
A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability
As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor
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