Reduction of computational effort in satellite image matching

2012 ◽  
Vol 22 (1) ◽  
pp. 236-243 ◽  
Author(s):  
I. N. Kaveev ◽  
A. G. Tashlinskii ◽  
R. M. Kurbanaliev
Keyword(s):  
Image Matching ◽  
Satellite Image ◽  
Computational Effort

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

UAVs Safe Landing Using Range Images

2011 ◽  
Author(s):  
Emanuele Frontoni ◽  
Adriano Mancini ◽  
Primo Zingaretti
Keyword(s):  
Image Analysis ◽  
Image Matching ◽  
Autonomous Navigation ◽  
Satellite Image ◽  
Guidance System ◽  
Final Position ◽  
Aerial Vehicle ◽  
Feature Based ◽  
Uav Navigation ◽  
Remote User

In this paper a mixed vision-range based approach, based on Kinect technology, for safe landing of an Unmanned Aerial Vehicle (UAV) is proposed. The guidance system allows a remote user to define target areas from an high resolution aerial or satellite image to determine the waypoints of the navigation trajectory or the landing area. The system is based on our previous work on UAV navigation and landing: a feature-based image matching algorithms finds the natural landmarks and gives feedbacks to the control system for autonomous navigation and landing. An algorithm for safe landing areas detection is proposed, based on the use of 4D RGBD (Red, Green, Blue, Distance) image analysis. The helicopter is required to navigate from an initial to a final position in a partially known environment, to locate a landing area and to land on it. Results show the appropriateness of the vision-based approach that does not require any artificial landmark (e.g., helipad) and is quite robust to occlusions, light variations and high vibrations.

scholarly journals Epipolar Rectification for CARTOSAT-1 Stereo Images Using SIFT and RANSAC

2014 ◽  
Vol XL-8 ◽  
pp. 1095-1098
Author(s):  
A. Akilan ◽  
D. Sudheer Reddy ◽  
V. Nagasubramanian ◽  
P. V. Radhadevi ◽  
G. Varadan
Keyword(s):  
Image Matching ◽  
Computational Effort ◽  
3D Modelling ◽  
High Fidelity ◽  
Stereo Images ◽  
Generation Algorithm ◽  
One Dimensional ◽  
Processing Step ◽  
Along Track ◽  
Modelling Application

Cartosat-1 provides stereo images of spatial resolution 2.5 m with high fidelity of geometry. Stereo camera on the spacecraft has look angles of +26 degree and -5 degree respectively that yields effective along track stereo. Any DSM generation algorithm can use the stereo images for accurate 3D reconstruction and measurement of ground. Dense match points and pixel-wise matching are prerequisite in DSM generation to capture discontinuities and occlusions for accurate 3D modelling application. Epipolar image matching reduces the computational effort from two dimensional area searches to one dimensional. Thus, epipolar rectification is preferred as a pre-processing step for accurate DSM generation. In this paper we explore a method based on SIFT and RANSAC for epipolar rectification of cartosat-1 stereo images.

Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features

2016 ◽  
pp. 135-171 ◽  
Author(s):  
Mohamed Tahoun ◽  
Abd El Rahman Shabayek ◽  
Hamed Nassar ◽  
Marcello M. Giovenco ◽  
Ralf Reulke ◽  
...  
Keyword(s):  
Comparative Study ◽  
Image Matching ◽  
Satellite Image ◽  
Local Features

scholarly journals Surface displacements in the September 2005 Afar rifting event from satellite image matching: Asymmetric uplift and faulting

2009 ◽  
Vol 36 (7) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ivana Barisin ◽  
Sebastien Leprince ◽  
Barry Parsons ◽  
Tim Wright
Keyword(s):  
Image Matching ◽  
Satellite Image ◽  
Surface Displacements

Block-and-octave constraint SIFT with multi-thread processing for VHR satellite image matching

2017 ◽  
Vol 8 (12) ◽  
pp. 1180-1189 ◽  
Author(s):  
Siliang Du ◽  
Mi Wang ◽  
Shenghui Fang
Keyword(s):  
Image Matching ◽  
Satellite Image

scholarly journals STATE OF THE ART IN DENSE IMAGE MATCHING COST COMPUTATION FOR HIGH-RESOLUTION SATELLITE STEREO

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 109-114
Author(s):  
Y. Wang ◽  
D. Gong ◽  
H. Hu ◽  
S. Wang ◽  
Y. Han ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Matching ◽  
Large Scale ◽  
Satellite Image ◽  
Ground Truth ◽  
Surface Model ◽  
Dense Image ◽  
High Resolution Satellite Images ◽  
Matching Cost ◽  
End To End

Abstract. Large-scale Digital Surface Model (DSM) generated with high-resolution satellite images (HRSI) are comparable, cheaper, and more accessible when comparing to Light Detection and Ranging (LiDAR) data and aerial remotely sensed images. Several photogrammetric commercial/open-source software packages are being developed for satellite image-based 3D reconstruction, in which, most of them adopt a modified version of Semi-Global Matching (SGM) algorithm for dense image matching. With the continuous development of matching cost computation methods, the existing methods can be divided into classical (low-level) and learning-based algorithms (non-end-to-end learning and end-to-end learning methods). On Middlebury and KITTI datasets, learning-based algorithms has shown their superiority compared to SGM derived methods. In this context, we assume that matching cost is the key factor of DIM. This paper reviews and evaluates Census Transform, and MC-CNN on a WorldView-3 typical city scene satellite stereo images on the premise that the overall SGM framework remains unchanged, providing a preliminary comparison for academic and industrial. We first compute the cost valume of these two methods, obtains the final DSM after semi-global optimization, and compares their gemetric accuracy with the corresponding LiDAR derived ground truth. We presented our comparison and findings in the experimental section.

scholarly journals SUPERVISED CLASSIFICATION AND ITS REPEATABILITY FOR POINT CLOUDS FROM DENSE VHR TRI-STEREO SATELLITE IMAGE MATCHING USING MACHINE LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 525-532
Author(s):  
A-M. Loghin ◽  
N. Pfeifer ◽  
J. Otepka-Schremmer
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Image Matching ◽  
Laser Scanning ◽  
Satellite Image ◽  
Point Clouds ◽  
Supervised Machine Learning ◽  
Ground Sample ◽  
Machine Learning Classification ◽  
3D Point Clouds

Abstract. Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.

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