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17 pages, 16952 KiB

Open AccessArticle

Best Scanline Determination of Pushbroom Images for a Direct Object to Image Space Transformation Using Multilayer Perceptron

by Seyede Shahrzad Ahooei Nezhad, Mohammad Javad Valadan Zoej, Kourosh Khoshelham, Arsalan Ghorbanian, Mahdi Farnaghi, Sadegh Jamali, Fahimeh Youssefi and Mehdi Gheisari

Remote Sens. 2024, 16(15), 2787; https://doi.org/10.3390/rs16152787 - 30 Jul 2024

Cited by 1 | Viewed by 540

Abstract

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods [...] Read more.

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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24 pages, 12981 KiB

Open AccessArticle

The Effect of Varying the Light Spectrum of a Scene on the Localisation of Photogrammetric Features

by Pawel Burdziakowski

Remote Sens. 2024, 16(14), 2644; https://doi.org/10.3390/rs16142644 - 19 Jul 2024

Viewed by 646

Abstract

In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., [...] Read more.

In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., demosaicing and conversion to greyscale, among others. These algorithms respond differently to the varying light spectrum of the scene, which consequently results in the feature location changing. In this study, the effect of scene illumination on the localisation of a feature in an image is presented. The demosaicing and greyscale conversion algorithms that produce the largest and smallest deviation of the feature from the reference point were assessed. Twelve different illumination settings from polychromatic light to monochromatic light were developed and performed, and five different demosaicing algorithms and five different methods of converting a colour image to greyscale were analysed. A total of 300 different cases were examined. As the study shows, the lowest deviation in the polychromatic light domain was achieved for light with a colour temperature of 5600 K and 5000 K, while in the monochromatic light domain, it was achieved for light with a green colour. Demosaicing methods have a significant effect on the localisation of a feature, and so the smallest feature deviation was achieved for smooth hue-type demosaicing, while for greyscale conversion, it was achieved for the mean type. Demosaicing and greyscale conversion methods for monochrome light had no effect. The article discusses the problem and concludes with recommendations and suggestions in the area of illuminating the scene with artificial light and the application of the algorithms, in order to achieve the highest accuracy using photogrammetric methods. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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20 pages, 8607 KiB

Open AccessArticle

Exterior Orientation Parameter Refinement of the First Chinese Airborne Three-Line Scanner Mapping System AMS-3000

by Hao Zhang, Yansong Duan, Wei Qin, Qi Zhou and Zuxun Zhang

Remote Sens. 2024, 16(13), 2362; https://doi.org/10.3390/rs16132362 - 27 Jun 2024

Viewed by 518

Abstract

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement [...] Read more.

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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19 pages, 5854 KiB

Open AccessArticle

Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

by Zhixin Li, Shuang Li, John Anderson and Jie Shan

Remote Sens. 2024, 16(5), 801; https://doi.org/10.3390/rs16050801 - 25 Feb 2024

Viewed by 1139

Abstract

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, [...] Read more.

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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20 pages, 5848 KiB

Open AccessArticle

A Bi-Radial Model for Lens Distortion Correction of Low-Cost UAV Cameras

by Frank Liebold, David Mader, Hannes Sardemann, Anette Eltner and Hans-Gerd Maas

Remote Sens. 2023, 15(22), 5283; https://doi.org/10.3390/rs15225283 - 8 Nov 2023

Cited by 2 | Viewed by 1203

Abstract

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application [...] Read more.

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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25 pages, 14653 KiB

Open AccessArticle

2OC: A General Automated Orientation and Orthorectification Method for Corona KH-4B Panoramic Imagery

by Zhuolu Hou, Yuxuan Liu, Li Zhang, Haibin Ai, Yushan Sun, Xiaoxia Han and Chenming Zhu

Remote Sens. 2023, 15(21), 5116; https://doi.org/10.3390/rs15215116 - 26 Oct 2023

Viewed by 1510

Abstract

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and [...] Read more.

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and 1970s for surveying-related tasks. This limitation hampers their significant potential in the remote sensing of the environment, urban planning, and other applications. This study proposes a method called 2OC for the automatic and accurate orientation and orthorectification of Corona KH-4B images, which is based on generalized control information from reference images such as Google Earth orthophoto. (1) For the Corona KH-4B panoramic camera, we propose an adaptive focal length variation model that ensures accuracy and consistency. (2) We introduce a robust multi-source remote sensing image matching algorithm, which includes an accurate primary orientation estimation method, a multi-threshold matching enhancement strategy based on scale, orientation, and texture (MTE), and a model-guided matching strategy. These techniques are employed to extract high-accuracy generalized control information for Corona images with significant geometric distortions and numerous weak texture areas. (3) A time-iterative Corona panoramic digital differential correction method is proposed. The orientation and orthorectification results of KH-4B images from multiple regions, including the United States, Russia, Austria, Burkina Faso, Beijing, Chongqing, Gansu, and the Qinghai–Tibet Plateau in China, demonstrate that 2OC not only achieves automation but also attains a state-of-the-art level of generality and accuracy. Specifically, the standard deviation of the orientation is less than 2 pixels, the mosaic error of orthorectified images is approximately 1 pixel, and the standard deviation of ground checkpoints is better than 4 m. In addition, 2OC can provide a longer time series analysis of data from 1962 to 1972, benefiting various fields such as environmental remote sensing and archaeology. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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20 pages, 17728 KiB

Open AccessArticle

Highly Efficient Anchor-Free Oriented Small Object Detection for Remote Sensing Images via Periodic Pseudo-Domain

by Minghui Wang, Qingpeng Li, Yunchao Gu and Junjun Pan

Remote Sens. 2023, 15(15), 3854; https://doi.org/10.3390/rs15153854 - 3 Aug 2023

Cited by 2 | Viewed by 1279

Abstract

With the continuous progress of remote sensing image object detection tasks in recent years, researchers in this field have gradually shifted the focus of their research from horizontal object detection to the study of object detection in arbitrary directions. It is worth noting [...] Read more.

With the continuous progress of remote sensing image object detection tasks in recent years, researchers in this field have gradually shifted the focus of their research from horizontal object detection to the study of object detection in arbitrary directions. It is worth noting that some properties are different from horizontal object detection during oriented object detection that researchers have yet to notice much. This article presents the design of a straightforward and efficient arbitrary-oriented detection system, leveraging the inherent properties of the orientation task, including the rotation angle and box aspect ratio. In the detection of low aspect ratio objects, the angle is of little importance to the orientation bounding box, and it is even difficult to define the angle information in extreme categories. Conversely, in the detection of objects with high aspect ratios, the angle information plays a crucial role and can have a decisive impact on the quality of the detection results. By exploiting the aspect ratio of different targets, this letter proposes a ratio-balanced angle loss that allows the model to make a better trade-off between low-aspect ratio objects and high-aspect ratio objects. The rotation angle of each oriented object, which we naturally embed into a two-dimensional Euclidean space for regression, thus avoids an overly redundant design and preserving the topological properties of the circular space. The performance of the UCAS-AOD, HRSC2016, and DLR-3K datasets show that the proposed model in this paper achieves a leading level in terms of both accuracy and speed. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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22 pages, 6992 KiB

Open AccessArticle

AHF: An Automatic and Universal Image Preprocessing Algorithm for Circular-Coded Targets Identification in Close-Range Photogrammetry under Complex Illumination Conditions

by Hang Shang and Changying Liu

Remote Sens. 2023, 15(12), 3151; https://doi.org/10.3390/rs15123151 - 16 Jun 2023

Viewed by 1341

Abstract

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic [...] Read more.

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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19 pages, 7823 KiB

Open AccessArticle

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

by Takashi Fuse and Kazuki Imose

Remote Sens. 2023, 15(11), 2714; https://doi.org/10.3390/rs15112714 - 23 May 2023

Viewed by 1327

Abstract

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic [...] Read more.

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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14 pages, 2493 KiB

Open AccessCommunication

Fighting Illicit Trafficking of Cultural Goods—The ENIGMA Project

by Petros Patias and Charalampos Georgiadis

Remote Sens. 2023, 15(10), 2579; https://doi.org/10.3390/rs15102579 - 15 May 2023

Cited by 3 | Viewed by 2260

Abstract

Cultural heritage is a testimony of past human activity, and, as such, cultural objects exhibit great variety in their nature, size, and complexity, from small artefacts and museum items to cultural landscapes, and from historic buildings and ancient monuments to city centers and [...] Read more.

Cultural heritage is a testimony of past human activity, and, as such, cultural objects exhibit great variety in their nature, size, and complexity, from small artefacts and museum items to cultural landscapes, and from historic buildings and ancient monuments to city centers and archaeological sites. Cultural heritage around the globe suffers from wars, natural disasters, and human negligence. More specifically, cultural goods and artefacts are put at risk through several anthropogenic actions: Anthropogenic threats take various dimensions, ranging from theft from museums, private collections, and religious buildings, smuggling of and illicit trade in cultural goods, the irremediable looting and demolition of archaeological sites by clandestine excavators, or simply neglect of heritage sites. Illicit trading has expanded dramatically recently, especially in areas affected by armed conflicts and natural disasters, either aiming at destroying collective memory and dismembering people’s identity or mostly motivated by the pursuit of profit. Moreover, the illicit trafficking of cultural goods contributes to the funding of terrorism, organized crime, and money laundering. The mission of ENIGMA, a EUR 4 million EU funded project, is to achieve excellence in the protection of cultural goods and artefacts from man-made threats by contributing to their identification, traceability, and provenance research, as well as by safeguarding and monitoring endangered heritage sites. ENIGMA objectives are designed to help the involved stakeholders better respond to this complex and multi-dimensional problem and leverage active collaboration by fostering and enabling interlinking of databases, and evidence-based deployment of preventative measures. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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Review

Jump to: Research

18 pages, 4845 KiB

Open AccessReview

Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring

by Sultan Kocaman and Gabriela Seiz

Remote Sens. 2023, 15(18), 4575; https://doi.org/10.3390/rs15184575 - 17 Sep 2023

Viewed by 1342

Abstract

This article reviews the role that photogrammetry plays in evaluating the geometric quality of satellite products in connection to the long-term monitoring of essential climate variables (ECVs). The Global Climate Observing System (GCOS) is responsible for defining the observations required for climate monitoring. [...] Read more.

This article reviews the role that photogrammetry plays in evaluating the geometric quality of satellite products in connection to the long-term monitoring of essential climate variables (ECVs). The Global Climate Observing System (GCOS) is responsible for defining the observations required for climate monitoring. Only satellite products are capable of providing high-quality observations of a particular subset of ECVs on a global scale. Geometric calibration and validation of these products are crucial for ensuring the coherence of data obtained across platforms and sensors and reliable monitoring in the long term. Here, we analyzed the GCOS implementation plan and the data quality requirements and explored various geometric quality aspects, such as internal and external accuracy and band-to-band registration assessment, for a number of satellite sensors commonly used for climate monitoring. Both geostationary (GEO) and low-earth orbit (LEO) sensors with resolutions between 250 m and 3 km were evaluated for this purpose. The article highlights that the geometric quality issues vary with the sensor, and regular monitoring of data quality and tuning of calibration parameters are essential for identifying and reducing the uncertainty in the derived climate observations. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

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