Geological remote sensing

Bishop, Charlotte A.; Rivard, Benoît; Filho, Carlos Roberto de Souza; Meer, Freek D. van der

doi:10.1016/j.jag.2017.08.005

Cited by 16 publications

(8 citation statements)

References 72 publications

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“…Geological remote sensing is a typical application of RS image interpretation, which can be considered as the study of geological survey and resource investigation with the electromagnetic spectrum based on the comprehensive utilization of remote sensing technologies [91]. Especially with the launch of hyper-spectral instruments to observe the Earth, the RS image is full of variant information and potential relationships beyond the capability of the traditional RS image interpretation method [92].…”

Section: Case Studymentioning

confidence: 99%

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Sun

Dustdar

Ranjan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, remote sensing image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of remote sensing images is still lacking. Specifically, this article discusses the main challenges of remote sensing image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

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Section: Case Studymentioning

confidence: 99%

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Sun

Dustdar

Ranjan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Sometimes, improper extrapolation of the interpretation of geological features in the field, like structures and lithological boundaries, produces inaccurate geological maps. Since the seventies (Bishop et al, 2018a), satellite imagery, with various missions and instruments, has allowed geological mapping to take a big step forward with the use of remotely sensed data. Geological remote sensing that debuted recently (Gregory, 1979)with the use of low resolution images of landsat1; has evolved in parallel with the progression of the satellite programs that followed; like Landsat2 to Landsat8; ASTER and others Multi or hyperspectral satellites, for more details see (("Satellite Missions Directory -Earth Observation Missions -eoPortal," n.d.).…”

Section: Introductionmentioning

confidence: 99%

“…Many authors reviewed various methods and various spectral processing for geological mapping e.g. (Asadzadeh and de Souza Filho, 2016;Bishop et al, 2018b;van der Meer et al, 2012)multiple reviews were about enhancements used for SWIR and VNIR bands of ASTER and/or Landsat images. Recently launched, Sentinel-2 with its Multi-Spectral instrument has shown its effectiveness in several studies on different subjects.…”

Section: Introductionmentioning

confidence: 99%

“…He has also demonstrated a good correspondence between those bands. In the present paper; based on the geological studies carried in the region of Tafilalet (Baidder et al, 2016(Baidder et al, , 2008Lubeseder et al, 2010;Michard et al, 2008;Soulaimani and Burkhard, 2008) and based on the remote sensing methods used in such geological mapping studies (Bishop et al, 2018b;Fagbohun et al, 2017;Rajendran and Nasir, 2017;Asadzadeh and de Souza Filho, 2016;van der Meer et al, 2012;Bannari et al, 2015Bannari et al, , 2016; we try to deduce the best improvement procedure (OIF, DS or MNF) that can be used to discriminate rock lithology encountered in the region. We also aim to classify lithofacies by processing sentinel 2A multispectral data.…”

Section: Introductionmentioning

confidence: 99%

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The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

Fal

Maanan

Baïdder

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Geological mapping in desert, mountainous or densely vegetated areas are sometimes faced with many constraints. Recently several remote sensing methods are used on ASTER or LANDSAT imagery for making that task easier. The aim of this paper is to evaluate the applicability of some of these methods on Sentinel-2A images. The study, therefore, focuses on a lithological classification using these multispectral images in the south of the Tafilalet basin. To achieve this goal, two L1C level images were used. Decorelation stretch combined with the optimal index factor (OIF) and Minimum Noise Fraction (MNF) were the main improvements used for RGB combination images. The classifiers Spectral Angle Mapper (SAM) and the Maximum Likelihood classifier (MLC) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest drawn geological maps and RGB images of false colour combinations were used to select regions of interest (ROI) as the endmembers to use for these classifiers. Obtained results showed a clear discrimination of the different lithological units of the study area. Classifications evaluation showed that the Maximum likelihood classifier is more accurate with an overall accuracy of 76% and a Kappa coefficient is 0.74. Finally, this study has shown the importance of the use of sentinel-2 multispectral images in geological mapping and has shown that the high spectral resolution of the VNIR and SWIR bands creates a synergy with the high spatial resolution for optimal lithological mapping.</p>

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“…The term remote sensing was invented to describe the new field of study that encompassed the development of multispectral camera's, infrared films, and nonphotographic scanners during the 1960s (Beckman Jr & Whitten, 1966;Bishop et al, 2018;Goetz & Rowan, 1981;Milton et al, 2009). The launch of the iconic Landsat-1 satellite in 1972 marks the introduction of the first Earth orbiting commercial multispectral data set (Lawrence & Herzog, 1975).…”

Section: Introductionmentioning

confidence: 99%

The influence of geologic sample surfaces on TIR spectroscopy

Rost¹

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Discussion3.5.1 Research achievements 3.5.2 Research limitations 3.5.3 Implications and future research 3.6 Conclusions Chapter 4 The impact of thermal infrared spectroscopy techniques and geological sample surface preparation: variability among TIR laboratories 4.1 Introduction 4.2 Data and methods 4.2.1 Sample characteristics 4.2.2 Sample preparation 4.2.3 Spectral measurements: devices and laboratory setups 4.2.4 Spectral statistical analyses 4.3 Results 4.3.1 Spectral measurements 4.3.2 Spectral statistical analyses 4.3.3 Summary of results 4.4 Discussion 4.4.1 Impact of instrument geometry 4.4.2 Impact of the sample preparation 4.4.3 Limitations of study 4.4.4 Best-practice-path 4.5 Conclusions Chapter 5 Synthesis 5.1 Progress made 5.2 Impact on various research fields 5.3 Future directions of research Summary Samenvatting Appendices Appendix 1 Appendix 2 Appendix 3 Appendix 4 References

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Geological remote sensing

Cited by 16 publications

References 72 publications

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

The influence of geologic sample surfaces on TIR spectroscopy

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