Automatic Satellite Images Orthorectification Using K–Means Based Cascaded Meta-Heuristic Algorithm

Mezouar, Oussama; Meskine, Fatiha; Boukerch, Issam

doi:10.14358/pers.22-00113r2

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Finally, a comparison between the computed image coordinates (l, s) and the correct image coordinates (l ′ , s ′ ) was conducted to analyze locational errors. This study utilized a hybrid method of NCC (normalized cross-correlation) and RECC (relative edge cross-correlation) for image matching [13].…”

Section: Dense Gcp Acquisitionmentioning

confidence: 99%

“…The second step in acquiring Kompsat-3A image coordinates involves image matching. In this study, we employed both area-matching (Figure 10) and edge-matching techniques (Figure 11) to address the substantial spectral differences between the aerial GCP chip and the targeted Kompsat-3A image [13]. These differences arise from variations in sensors, acquisition dates, weather conditions, and acquisition angles.…”

Section: Gcp Chip Generationmentioning

confidence: 99%

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The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

Jo,

Lee,

2024

Applied Sciences

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The vast potential of high-resolution satellite images, including Kompsat-3A, has been demonstrated across diverse applications, such as mapping and disaster monitoring. However, these images can only be utilized as reliable GIS (geographic information system) data when they possess precise geographical information. To achieve this, sensor model information, represented by RPCs (rational polynomial coefficients), requires bias compensation through GCPs (ground control points). Though having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility. The uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process, which is the focus of this study. Experiments were meticulously conducted using Kompsat-3A data using dense GCP information. The dense GCP information was automatically generated from aerial orthoimages through a three-step process. Firstly, the GCP chips were extracted from the aerial images, focusing on feature points. Secondly, these chips were projected onto the target Kompsat-3A data to align them accurately. Lastly, precise satellite image coordinates of the chips were obtained through image matching between the chips and the target Kompsat-3A image. The dense GCPs enabled detailed bias analysis that exhibited skewness in most Kompsat-3A data. This necessitates the implementation of an affine model for proper bias compensation over the entire image space. Next, the study delved into the influence of GCP distribution on RPC bias compensation. To this end, each target satellite image space was divided into nine zones, with the dense GCPs assigned accordingly. The accuracy of bias compensation was analyzed across nine experimental cases, ranging from GCPs occupying only one zone to GCPs covering all nine zones. It was observed that GCPs covering at least four or five zones should be utilized for reliable RPC bias compensation of Kompsat-3A, especially when aiming for a high level of accuracy with an RMSE of one pixel. Finally, it was concluded that GCPs covering three zones yielded satisfactory results as a minimum GCP requirement, but this was contingent upon their distribution not following a straight zone pattern.

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Section: Dense Gcp Acquisitionmentioning

confidence: 99%

Section: Gcp Chip Generationmentioning

confidence: 99%

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

Jo,

Lee,

2024

Applied Sciences

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Image Fusion in Remote Sensing: An Overview and Meta-Analysis

Albanwan,

Qin,

Tang

2024

photogramm eng remote sensing

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Remote sensing image fusion is consistently used to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatiotemporally coherent images. It facilitates downstream applications such as pan sharpening, change detection, and classification. However, image fusion solutions are highly disparate to various remote sensing problems and are often narrowly defined in existing reviews as topical applications (e. g., pan sharpening). Theoretically, image fusion can be applied to any gridded data through pixel-level operations; thus, we expand its scope by comprehensively surveying relevant works. We develop a simple taxonomy for many-to-one and many-to-many image fusion, defining it as a mapping problem turning one or multiple images into another set based on desired coherence. Furthermore, we provide a meta-analysis to cover 10,420 peer-reviewed papers from the 1980s to 2023 studying various types of image fusion and their applications. Finally, we discuss image fusion's benefits and emerging challenges to provide open research directions.

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Automatic Satellite Images Orthorectification Using K–Means Based Cascaded Meta-Heuristic Algorithm

Cited by 2 publications

References 23 publications

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

Image Fusion in Remote Sensing: An Overview and Meta-Analysis

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