scholarly journals An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

2022 ◽  
Author(s):  
Zhiyuan You ◽  
Junzheng Li ◽  
Hongcheng Zhang ◽  
Bo Yang ◽  
Xinyi Le
Keyword(s):  
Minimum Spanning Tree ◽  
Graph Matching ◽  
Edge Weight ◽  
Attitude Measurement ◽  
Spectral Graph ◽  
Neighbor Graph ◽  
Star Identification ◽  
Dynamic Search ◽  
Global Matching ◽  
Identification Approach

AbstractStar identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

scholarly journals MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 247-255 ◽  
Author(s):  
Y. Di ◽  
G. Jiang ◽  
L. Yan ◽  
H. Liu ◽  
S. Zheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Minimum Spanning Tree ◽  
Network Evolution ◽  
Edge Weight ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Multiple Features ◽  
Multi Scale ◽  
Initial Segmentation

Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.

scholarly journals A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION

2016 ◽  
Vol III-7 ◽  
pp. 111-117
Author(s):  
Ping Wang ◽  
Zheng Wei ◽  
Weihong Cui ◽  
Zhiyong Lin
Keyword(s):  
Image Segmentation ◽  
Coastal Area ◽  
Minimum Spanning Tree ◽  
Edge Weight ◽  
Segmentation Method ◽  
Optimal Criterion ◽  
The Rich ◽  
Uav Images ◽  
Uav Image ◽  
Scale Control

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

scholarly journals On the Length of a Random Minimum Spanning Tree

2015 ◽  
Vol 25 (1) ◽  
pp. 89-107 ◽  
Author(s):  
COLIN COOPER ◽  
ALAN FRIEZE ◽  
NATE INCE ◽  
SVANTE JANSON ◽  
JOEL SPENCER
Keyword(s):  
Complete Graph ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Expected Value ◽  
Edge Weight ◽  
Formula Group ◽  
Image Position

We study the expected value of the lengthLnof the minimum spanning tree of the complete graphKnwhen each edgeeis given an independent uniform [0, 1] edge weight. We sharpen the result of Frieze [6] that limn→∞$\mathbb{E}$(Ln) = ζ(3) and show that$$ \mathbb{E}(L_n)=\zeta(3)+\frac{c_1}{n}+\frac{c_2+o(1)}{n^{4/3}}, $$wherec1,c2are explicitly defined constants.

Fast and scalable approximate spectral graph matching for correspondence problems

2013 ◽  
Vol 220 ◽  
pp. 306-318 ◽  
Author(s):  
U Kang ◽  
Martial Hebert ◽  
Soonyong Park
Keyword(s):  
Graph Matching ◽  
Spectral Graph

scholarly journals POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

2016 ◽  
Vol XLI-B3 ◽  
pp. 741-747 ◽  
Author(s):  
Shiyu Chen ◽  
Xiuxiao Yuan ◽  
Wei Yuan ◽  
Yang Cai
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Urban Areas ◽  
Graph Matching ◽  
Recall Rate ◽  
Edge Weight ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Series Of Experiments

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

scholarly journals CONSTRUCTING DEGREE-3 SPANNERS WITH OTHER SPARSENESS PROPERTIES

1996 ◽  
Vol 07 (02) ◽  
pp. 121-135 ◽  
Author(s):  
GAUTAM DAS ◽  
PAUL J. HEFFERNAN
Keyword(s):  
Euclidean Space ◽  
Shortest Path ◽  
Spanning Tree ◽  
Maximum Degree ◽  
Euclidean Distance ◽  
Minimum Spanning Tree ◽  
Dimensional Euclidean Space ◽  
Edge Weight

Let V be any set of n points in k-dimensional Euclidean space. A subgraph of the complete Euclidean graph is a t-spanner if for all u and υ in V, the length of the shortest path from u to υ in the spanner is at most t times the Euclidean distance between u and υ. We show that for any δ>1, there exists a t-spanner (where t is a constant that depends only on δ and k) with the following properties: its maximum degree is 3, it has at most n·δ edges, its total edge weight is at most O(1) times the weight of the minimum spanning tree of V, and it can be constructed in O(n log n) time. The constants implicit in the O-notation depend on δ and k.

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