A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Daliakopoulos, Ioannis Ν.; Tsanis, Ioannis K.

doi:10.3390/s19051123

Cited by 8 publications

(4 citation statements)

References 55 publications

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“…If D(x) ≥ 0.03, the feature point is retained; otherwise, it is discarded. SIFT algorithm makes use of the gradient direction distribution characteristics of neighborhood pixels of key points to specify direction parameters for each key point, so that the operator has rotation invariance [15].…”

Section: Feature Points Extraction Of Cardiac Ultrasound Imagesmentioning

confidence: 99%

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

Zhang¹,

Li²,

2021

Journal of Healthcare Engineering

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The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms ( P < 0.01 ). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method ( P < 0.05 ), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method ( P > 0.05 ). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.

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Section: Feature Points Extraction Of Cardiac Ultrasound Imagesmentioning

confidence: 99%

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

Zhang¹,

Li²,

2021

Journal of Healthcare Engineering

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“…In this article, a dilated conventional residual network and a mixed distance measurement algorithm are presented for these two works. For image feature extraction, unlike traditional feature extraction methods, such as SIFT, 3,4 which can only extract the shallow details of clothing. We develop a kind of fashion image feature extraction model based on dilated convolutional residual network.…”

Section: Introductionmentioning

confidence: 99%

DCR‐Net: Dilated convolutional residual network for fashion image retrieval

Jia

Yuan

Zhang

et al. 2022

Computer Animation & Virtual

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Fashion image retrieval is an important branch of image retrieval technology.With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text-based to content-based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi-scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high-dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high-dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state-of-the-art methods.

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“…Recent advances in the availability of Very High Resolution (VHR) satellite imagery, satellites equipped with sensors acquiring at a metric and sub-metric spatial resolution and the possibility of acquiring stereo imagery capable of covering large areas have led to an increasing trend of their applications in various fields, such as for bathymetry extraction, the construction of 3D models of urban environments, etc. [9][10][11][12]. One of the first approaches using VHR satellite images for reconstructing urban scenes in LOD1 was the semi-global matching technique (SGM), which uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery

Pepe

Costantino

Alfio

et al. 2021

IJGI

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The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined; this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F1 measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way.

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A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Cited by 8 publications

References 55 publications

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

DCR‐Net: Dilated convolutional residual network for fashion image retrieval

A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery

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