Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

Maiwald, Ferdinand; Feurer, Denis; Eltner, Anette

doi:10.1016/j.isprsjprs.2023.11.008

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…Consolidated photogrammetric techniques, such as Structurefrom-Motion (SfM) and Multi-View-Stereo (MVS) dense matching can be successfully used to accomplish the 3D reconstruction process based on every type of images (from drones, aircrafts, natively digital or digitized from analogue photos), as proved in previous works (see, e.g., James et al, 2019). New matching methods based on AI-algorithms have been also applied for feature and dense matching between images with strong geometric/radiometric changes, which are typical of the mountain environment (see Maiwald et al, 2023;Morelli et al, 2024). On the other hand, their application is not yet implemented in the routinary processing of photogrammetric blocks.…”

Section: Motivationsmentioning

confidence: 94%

Multitemporal Structure-from-Motion: A Flexible Tool to Cope with Aerial Blocks in Changing Mountain Environment

Genzano,

Fugazza,

Eskandari

et al. 2024

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

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Abstract. The application of Structure-from-Motion (SfM) and Multi-View-Stereo matching with aerial images can be successfully used for deriving dense point clouds to analyse changes in the mountain environment, which is characterized by changes due to the action of natural process. The comparison of multiple datasets requires to setup a stable reference system, task that is generally implemented by means of ground control points (GCPs). On the other hand, their positioning may be sometimes difficult in mountains. To cope with this drawback an approach termed as Multitemporal SfM (MSfM) is presented: multiple blocks are oriented together within a unique SfM project, where GCPs are used in only one epoch for establishing the absolute datum. Accurate coregistration between different epochs depends on the automatic extraction of tie points in stable areas. To verify the application of MSfM in real cases, this paper presents three case studies where different types of photogrammetric data are adopted, including images from drones and manned aircrafts. Applications to glacier and mountain river erosion are entailed.

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Section: Motivationsmentioning

confidence: 94%

Multitemporal Structure-from-Motion: A Flexible Tool to Cope with Aerial Blocks in Changing Mountain Environment

Genzano,

Fugazza,

Eskandari

et al. 2024

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

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“…Over the last decade, there has been a proliferation of deep learning (DL) approaches for feature extraction and matching (Chen et al, 2021;Jin et al 2021;Yao et al, 2021) that aim to overcome these limitations and they have demonstrated resilience against varying illumination conditions, multitemporal datasets, wide baselines, and significantly different view angles. Recently, several works have proved the effectiveness of DL approaches in challenging scenarios, including glacier monitoring with wide camera baselines (Ioli et al, 2023a, Ioli et al, 2023b, multi-temporal image matching (Maiwald et al, 2023), multi-temporal co-registration problems (Maiwald et al, 2021;, VO and SLAM (Morelli et al, 2023), aerial triangulation (Remondino et al, 2022) and in terrestrial laser scanning point cloud registration (Markiewicz et al, 2023). However, well known limitations of DL approaches are their computational complexity, limited scale and rotation invariance of the descriptors and their application on high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Deep-Image-Matching: A Toolbox for Multiview Image Matching of Complex Scenarios

Morelli,

Ioli,

Maiwald

et al. 2024

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

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Abstract. Finding corresponding points between images is a fundamental step in photogrammetry and computer vision tasks. Traditionally, image matching has relied on hand-crafted algorithms such as SIFT or ORB. However, these algorithms face challenges when dealing with multi-temporal images, varying radiometry and contents as well as significant viewpoint differences. Recently, the computer vision community has proposed several deep learning-based approaches that are trained for challenging illumination and wide viewing angle scenarios. However, they suffer from certain limitations, such as rotations, and they are not applicable to high resolution images due to computational constraints. In addition, they are not widely used by the photogrammetric community due to limited integration with standard photogrammetric software packages. To overcome these challenges, this paper introduces Deep-Image-Matching, an open-source toolbox designed to match images using different matching strategies, ranging from traditional hand-crafted to deep-learning methods (https://github.com/3DOM-FBK/deep-image-matching). The toolbox accommodates high-resolution datasets, e.g. data acquired with full-frame or aerial sensors, and addresses known rotation-related problems of the learned features. The toolbox provides image correspondences outcomes that are directly compatible with commercial and open-source software packages, such as COLMAP and openMVG, for a bundle adjustment. The paper includes also a series of cultural heritage case studies that present challenging conditions where traditional hand-crafted approaches typically fail.

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“…In the case of images acquired for mapping purposes with flights conducted under adequate weather conditions and stereo-coverage/overlap, the potential of their digitization is amplified by the possibility of extracting geometric information through photogrammetric processes [22,24]. The development in the last years of more efficient tools and algorithms for the 3D processing of digital images and the increasing automation of the reconstruction process opened new opportunities for the full exploitation of historical images [23,25,26]. The fully automatic matching of image features included in current photogrammetric software is typically based on traditional hand-crafted approaches, such as SIFT [27], ORB [28], or SURF [29].…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32][33][34]. Learning-based methods started to be applied to historical images for 4D urban reconstruction purposes [26,35,36]. At the same time, the improved performance of conventional and learning-based dense image matching algorithms [37][38][39][40] open unprecedented chances of revitalizing vast collections of historical photographs through the extraction of detailed and accurate digital surface models (DSMs), facilitating scene understanding and supporting multi-temporal studies [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching

Kostrzewa,

Farella,

Morelli

et al. 2024

Applied Sciences

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In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical.

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Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

Cited by 6 publications

References 42 publications

Multitemporal Structure-from-Motion: A Flexible Tool to Cope with Aerial Blocks in Changing Mountain Environment

Multitemporal Structure-from-Motion: A Flexible Tool to Cope with Aerial Blocks in Changing Mountain Environment

Deep-Image-Matching: A Toolbox for Multiview Image Matching of Complex Scenarios

Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching

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