An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Min-Lung Cheng; Masashi Matsuoka

doi:10.3390/s21186035

An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Sensors ◽

10.3390/s21186035 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6035

Author(s):

Min-Lung Cheng ◽

Masashi Matsuoka

Keyword(s):

Image Matching ◽

Image Features ◽

Optical Image ◽

Feature Points ◽

Color Information ◽

Image Mosaicking ◽

Optical Image Processing ◽

Matching Technique ◽

Close Range ◽

Geometric Mapping

Matching local feature points is an important but crucial step for various optical image processing applications, such as image registration, image mosaicking, and structure-from-motion (SfM). Three significant issues associated with this subject have been the focus for years, including the robustness of the image features detected, the number of matches obtained, and the efficiency of the data processing. This paper proposes a systematic algorithm that incorporates the synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) method and the affine transformation with bounding box (ATBB) procedure to address these three issues. The SC-EABRISK approach selects the most representative feature points from an image and rearranges their descriptors by adding color information for more precise image matching. The ATBB procedure, meanwhile, is an outreach that implements geometric mapping to retrieve more matches from the feature points ignored during SC-EABRISK processing. The experimental results obtained using benchmark imagery datasets, close-range photos (CRPs), and aerial and satellite images indicate that the developed algorithm can perform up to 20 times faster than the previous EABRISK method, achieve thousands of matches, and improve the matching precision by more than 90%. Consequently, SC-EABRISK with the ATBB algorithm can address image matching efficiently and precisely.

Download Full-text

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Download Full-text

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.701 ◽

2011 ◽

Vol 121-126 ◽

pp. 701-704

Author(s):

Xue Tong Wang ◽

Yao Xu ◽

Feng Gao ◽

Jing Yi Bai

Keyword(s):

Image Matching ◽

Singular Value ◽

Feature Points ◽

Matching Algorithm ◽

Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

Download Full-text

Detection of abnormal cells by optical image processing

10.1117/12.486607 ◽

2002 ◽

Author(s):

Wilhelm R. A. Waidelich ◽

Peter J. Hutzler ◽

Raphaela M. Waidelich

Keyword(s):

Image Processing ◽

Optical Image ◽

Optical Image Processing ◽

Abnormal Cells

Download Full-text

DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-1005-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 1005-1009 ◽

Cited By ~ 1

Author(s):

S. Rhee ◽

T. Kim

Keyword(s):

Global Optimization ◽

Image Matching ◽

Point Clouds ◽

Image Space ◽

Object Space ◽

3D Point Clouds ◽

Local Point ◽

Matching Technique ◽

Uav Images ◽

Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

Download Full-text

Optical Image Processing In GaAs And BSO

10.1117/12.941604 ◽

1987 ◽

Author(s):

N B. Aldridge

Keyword(s):

Image Processing ◽

Optical Image ◽

Optical Image Processing

Download Full-text

All-optical image processing technology and applications based on micro-/nano-devices

Infrared and Laser Engineering ◽

10.3788/irla20201040 ◽

2020 ◽

Vol 49 (9) ◽

pp. 20201040-20201040

Author(s):

付伟伟 Weiwei Fu ◽

黄坤 Kun Huang

Keyword(s):

Image Processing ◽

Optical Image ◽

Processing Technology ◽

Image Processing Technology ◽

Optical Image Processing ◽

All Optical

Download Full-text

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2021 ◽

Vol 65 (1) ◽

pp. 10501-1-10501-9

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

Abstract The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Download Full-text