• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1281-1287
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• 2016

This paper presents a method of single image dehazing and feature matching for aerial remote sensing images. In the case of a aerial image, transferring the information of the original image is difficult as the contrast leans by the haze. This also causes that the image contrast decreases. Therefore, a refined transmission map based on a hidden Markov random field. Moreover, the proposed algorithm enhances the accuracy of image matching surface-based features in an aerial remote sensing image. The performance of the proposed algorithm is confirmed using a variety of aerial images captured by a Worldview-2 satellite.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.1-7
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• 2003

Practical use is increasing on the aerial ortho image recently, and much researches for geographic information system build that use high resolution satellite image cause this are progressing. Also many researches that use KOMPSAT-1 satellite image of resolution 6.6m are performing in these days, estimation for between aerial photo and satellite image is needed. In this treatise scanned image of aerial photo, using aerial photo resampling image of resolution equal with KOMPSAT-1 image using aerial photo, and KOMPSAT-1 satellite image use for experimental image making each orthoimage, classified feature for estimate. We evaluated to what level that an separation item could be able to estimate in each orthoimage. As result of estimation analysis, In the classified feature in aerial photo orthoimage with aerial photo resampling image orthoimage is about 61%, KOMPSAT-1 satellite image orthoimage is almost 41% could estimated. Through this investigation estimate, KOMPSAT-1 satellite sue to map updating, geographic information og non-approach area and environment inspect.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.259-262
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• 2004

There is the case to make the image map by using color aerial photo, DEM gives the accuracy of the Ortho image. Therefore We evaluate the accuracy by digital photogrammetric system after generating the DEM of Color Aerial Image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.4
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• pp.387-394
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• 2000

The biggest factor of errors which is constructed to the database of aerial photo image is happen to process of scanning with aerial photogrammetric scanner. For the quality assurance of aerial phto image database, we are need to the verification process of aerial photogrammetric scanner. This study is purposed to exhibit quality assurance method of aerial photo image database which is established to verification method of accuracy for aerial photogrammetric scanner. As the results of this study, we are established to verification method of accuracy for aerial photogrammetric scanner. And, the proposed methods according to this study are known to contributequality assurance of aerial photo image database.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.89-99
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• 2000

The Quality and Accuracy of digital image is important factor for decision of accuracy in digital photogrammetry because all the inside works in digital photogrammetry are based on digital image. But it is still difficult to ensure quality assurance and appication of data because there is no distinct criterion about quality and accuracy of digital image when the works in digital photogrammetry is accomplished. This study presents optimal resolution of aerial photo through error analysis of image coordinate using auto inner orientation in digital photograrnrnetry workstation. In second step, we are valified to optimum resolution of aerial photo image with orientation analysis. Finally, we are established to validity optimal resolution of aerial photo image with production of ortho image and mosaic image using optimal resolution aerial photo image.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.4
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• pp.131-136
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• 1999

Aerial videography techniques have been used to inventory conditions associated with grassland, forests, and agricultural crop production. Most recently, aerial videography has been used to verity satellite image classifications as part of the natural ecosystem survey. The objectives of this study were: (1) to use aerial video images of the study area, one part of Taean Seashore National Park, for the accuracy assessment, and (2) to determine the suitability of aerial videography as an accuracy assessment, of the land cover classification with Landsat-TM data. Video images were collected twice, summer and winter seasons, and divided into two kinds of images, wide angle and narrow angle images. Accuracy assessment methods include the calculation of the error matrix, the overall accuracy and kappa coefficient of agreement. This study indicates that aerial videography is an effective tool for accuracy assessment of the satellite image classifications of which features are relatively large and continuous. And it would be possible to overcome the limits of the present natural ecosystem survey method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3177-3195
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• 2014

In many moving object detection problems of an aerial video, accurate and robust stabilization is of critical importance. In this paper, a novel accurate image alignment algorithm for aerial electronic image stabilization (EIS) is described. The feature points are first selected using optimal derivative filters based Harris detector, which can improve differentiation accuracy and obtain the precise coordinates of feature points. Then we choose the Delaunay Triangulation edges to find the matching pairs between feature points in overlapping images. The most "useful" matching points that belong to the background are used to find the global transformation parameters using the projective invariant. Finally, intentional motion of the camera is accumulated for correction by Sage-Husa adaptive filtering. Experiment results illustrate that the proposed algorithm is applied to the aerial captured video sequences with various dynamic scenes for performance demonstrations.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.239-243
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• 2004

Recently various thematic maps and image maps using aerial photograph and satellite imagery are frequently made. The geo-referencing is essential to make image map and topographic map using aerial photograph and satellite imagery. For this geo-referencing, Ground Control Points (GCPs) are needed. In this paper, we used GPS relative positioning to measure GCP ground coordinate and the accuracy of 8cm level was achieved. We made GCP image chips for the efficiency of geo-referencing and carried out the bundle adjustment of aerial photographs using GCP image chips to acquire the GCP photo coordinate with image matching technique. Finally we analyzed the accuracy of bundle adjustment compared to the accuracy of the case in using digital maps to acquire GCP photo coordinate.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.52-54
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• 2022

Aerial images are being one of the important aspects of satellite imagery, delivers effective information on landcovers. Their special characteristics includes the viewpoint from space which clarifies data related to land examining processes. Aerial images taken by satellites employed radio waves to wirelessly transmit images to ground stations. Due to transmission errors, images get distorted and unable to perform in landcover examining. This paper proposes an aerial image enhancement method using deep autoencoders. A properly trained autoencoder can enhance an aerial image to a considerable level of improvement. Results showed that the achieved enhancement is better than that was obtained from traditional image denoising methods.