An automatic ground control point matching based on GCP chip database for remote sensing images

Chen, Q. H.; Liu, X. G.; Guo, Wen; Liu, T. L.

doi:10.1109/iasp.2009.5054640

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, another score threshold C t is set while performing NMS; when the score of the traversed region is less than C t , it indicates that the shape complexity of the feature region is high, and the feature region is immediately removed. The calculating process of NMS is shown in (6).…”

Section: Non-maximum Suppressionmentioning

confidence: 99%

“…The traditional method involves manually collecting and analyzing GCPs, which can be time-consuming, labor-intensive, and requires duplication of effort. To make better use of existing data, we can establish a control point database, which facilitates quick point matching [6]. Nevertheless, maintaining a control point database is complex and expensive.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Region Feature Extraction With Salient MSER and Segment Distance-Weighted GLOH for Remote Sensing Image Registration

Zhao,

Wang,

You

2024

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

View full text Add to dashboard Cite

Section: Non-maximum Suppressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Region Feature Extraction With Salient MSER and Segment Distance-Weighted GLOH for Remote Sensing Image Registration

Zhao,

Wang,

You

2024

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

View full text Add to dashboard Cite

“…The traditional method is to analyze the selected GCPs in the image manually, which is time-consuming and laborious, and frequently collects GCPs multiple times. The method of establishing a control point database can be used to retrieve control point image slices in the control point database to achieve quick matching of control points [12]. In addition, when geometric correction is performed on future remote sensing images, existing control points can be searched directly from the control point database to match the images, thus enabling the reuse of control points.…”

Section: Introductionmentioning

confidence: 99%

An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database

2023

View full text Add to dashboard Cite

Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than 30% and reduce the matching time by more than 40%. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images.

show abstract

An Automated Software Framework for Evaluation and Improvement of Absolute Geometric Location Accuracy of Multispectral Remote Sensing Satellite Imagery

Bhateja

Misra

Moorthi

et al. 2018

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

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Geometric accuracy is an important parameter for quality assessment of a data product and is vital for certain applications aiming on improving and bringing precision in data products. Automatic geometric accuracy evaluation of the satellite image is attempted by matching a known, surveyed location typically a ground control point (GCP) calibrated using a differential global positioning system (DGPS), verified using Google Maps, to the corresponding identifiable feature in an image product. The requirement for this development is to address the non-uniformity in the available data products in terms of coordinates reference system, resolution and available bands, which the software overcomes successfully by benefiting from the classes and functions available in openly available GIS libraries. RMSE of 0.8 pixels is found in analysis for the chosen data. Further, an algorithm is worked up to rectify the image for this geometric shift.</p>

show abstract

An automatic ground control point matching based on GCP chip database for remote sensing images

Cited by 3 publications

References 5 publications

Robust Region Feature Extraction With Salient MSER and Segment Distance-Weighted GLOH for Remote Sensing Image Registration

Robust Region Feature Extraction With Salient MSER and Segment Distance-Weighted GLOH for Remote Sensing Image Registration

An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database

An Automated Software Framework for Evaluation and Improvement of Absolute Geometric Location Accuracy of Multispectral Remote Sensing Satellite Imagery

Contact Info

Product

Resources

About