The simulation approach to the interpretation of archival aerial photographs

Siok, Katarzyna; Ewiak, I.

doi:10.1515/geo-2020-0001

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…However, when combining image data from such different platforms, it is expected that the resulting enhanced images will be encumbered with more spectral distortion than when combining data from the same platform. In previous studies [ 7 , 9 , 21 ], the authors proved that, despite the spectral distortions that occur, the enhanced images obtained by combining aerial and satellite data are useful for environmental analyses (interpretation and classification of land cover). In this article, the authors went one step further in developing the integration of aerial and satellite image data, and they presented the methodology for increasing the spectral quality of sharpened images, thus giving rise to the development of new techniques for combining images recorded at different altitudes.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, for data from different sensors, the ratio may be much higher, e.g., for aerial panchromatic data and multispectral satellite data (Sentinel-2), it may amount to 1:70. However, as the authors’ previous research has shown [ 7 , 8 , 9 ], with a ratio higher than 1:5, it is also possible to obtain a product that can improve terrain analysis compared to the interpretation of two separate products in the form of an aerial photo with a high spatial resolution and satellite imagery with a high spectral resolution. Another aspect is the co-registration of data.…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, methods of combining UAV and satellite optical data [ 5 , 8 , 13 , 14 ], methods of integrating data obtained from aircraft with satellite data [ 15 , 16 ], or methods of combining optical and radar satellite data [ 17 , 18 , 19 , 20 ] have been proposed. In their studies [ 7 , 21 , 22 ], the authors proved the increase in the interpretation potential of archival single-band aerial photographs as a result of combining these data with archival satellite images.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

Siok

Ewiak

Jenerowicz

2020

Sensors

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The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

Siok

Ewiak

Jenerowicz

2020

Sensors

Self Cite

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“…For these reasons, recent works were based on learning-based approaches to extract reliable correspondences among multi-temporal images and improve image matching (Ressl et al, 2020;Zhang et al, 2021). Historical aerial images have frequently been used to interpret the territory and discover lost information (Sevara, 2013), applying monoplotting (Bozzini et al, 2012;Bayr, 2021) but also using simulation processes for supporting data interpretation (Siok and Ewiak, 2020). Further attractive research activities have focussed on exploiting historical aerial photographs to identify war-related bomb craters (Meixner and Eckstein, 2016;Valjavec et al, 2018;Clermont et al, 2019;Dolejš et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

The Eurosdr Time Benchmark for Historical Aerial Images

Farella

Morelli

Remondino

et al. 2022

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

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Abstract. Automatic photogrammetric processing of historical (or archival) aerial photos is still a challenging task, particularly in cases of missing ancillary information, low radiometric and image quality, limited stereo coverage or large temporal span. However, with recent advances in photogrammetry and Artificial Intelligence (AI) algorithms for image processing and interpretation, an increasing number of applications are now feasible. The article presents the TIME (hisTorical aerIal iMagEs) benchmark (https://time.fbk.eu/), promoted by EuroSDR to explore the potential of historical aerial images. Realized in collaboration with various European NMCAs, the benchmark has garnered aerial image blocks and time series imagery captured since the 1950s. To support the photogrammetric processing of the digitized photos, ancillary data are supplied with available information about flight missions, taking cameras, and ground control points (GCPs). Several diverse investigations have been undertaken with the benchmark datasets, all captured over historical urban areas or landscapes. The paper describes the benchmark datasets and some potential research topics, presenting several tests and analyses realized with the collated and shared data.

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“…Several researchers have used aerial photographs as reference data [24][25][26]. They are highly valued because they are the only materials with high credibility on terrain coverage and use [27]. The reference data used for this research is an aerial photograph of the study area acquired on the 20 th of December 2006 by The GeoInformation Group http://www.geoinformationgroup.co.uk and has a spatial resolution of 0.125m.…”

Section: Data Collectionmentioning

confidence: 99%

Evaluation of Different Satellite Images for Urban Land Use Analysis: A Case Study of Greater Manchester, United Kingdom

Aliogo

Anyiam

2022

JGEESI

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Background: The complexity of an urban area makes mapping it very difficult as its surface materials are highly spatial and spectrally diverse. This study evaluates the problems associated with remote sensing of urban land cover types. Methodology: Three satellite image sensors; Disaster Monitoring Constellation (DMC), Landsat TM and colour infrared were used to investigate their potential in mapping and characterizing land cover in a part of Greater Manchester. Supervised and unsupervised image classifications were used to map urban land use and cover. Results: The satellite image sensors and their accuracy were statistically tested to see if there is a significant relationship between them. The colour infrared image was the best in discriminating among different types of land cover with an overall accuracy of 80% followed by the Landsat image with an overall accuracy of 61% while the DMC image had the least potential in discriminating among different types of land cover with an overall accuracy of 55%. Conclusion: The colour infrared image is the most suitable for urban land cover analysis as the misclassifications are minimal compared to the other two and the features can be vividly recognized due to its spatial resolution.

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The simulation approach to the interpretation of archival aerial photographs

Cited by 5 publications

References 24 publications

Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

The Eurosdr Time Benchmark for Historical Aerial Images

Evaluation of Different Satellite Images for Urban Land Use Analysis: A Case Study of Greater Manchester, United Kingdom

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