Real-time registration of remote sensing images with a Markov chain model

Song, Yinglei; Qu, Junle; Liu, Chunmei

doi:10.1007/s11554-020-01043-1

Search citation statements

Order By: Relevance

Paper Sections

Select...

Conventional Methods Of Statistical Computing1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2023

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [9] investigated automatic image registration of different remote sensing images of the same scene. The main problem in this task is that different images of the same scene usually have different spatial resolutions.…”

Section: Conventional Methods Of Statistical Computingmentioning

confidence: 99%

Real-time statistical image and video processing for remote sensing and surveillance applications

Khosravi

Tavallali

2021

J Real-Time Image Proc

View full text Add to dashboard Cite

Section: Conventional Methods Of Statistical Computingmentioning

confidence: 99%

Real-time statistical image and video processing for remote sensing and surveillance applications

Khosravi

Tavallali

2021

J Real-Time Image Proc

View full text Add to dashboard Cite

FPGA-accelerated adaptive cartesian to polar conversion and efficient MI computation for image registration

Mondal

2022

J Real-Time Image Proc

View full text Add to dashboard Cite

Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

Chen

Hua

2023

era

View full text Add to dashboard Cite

<abstract><p>With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio).</p></abstract>

show abstract

Real-time registration of remote sensing images with a Markov chain model

Cited by 3 publications

References 49 publications

Real-time statistical image and video processing for remote sensing and surveillance applications

Real-time statistical image and video processing for remote sensing and surveillance applications

FPGA-accelerated adaptive cartesian to polar conversion and efficient MI computation for image registration

Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

Contact Info

Product

Resources

About