scholarly journals Application of matlab for digital images topological characteristics calculation and analysis

2021 ◽  
Vol 60 (1) ◽  
pp. 72-78
Author(s):  
Olga V. Samarina ◽  
Valeriy A. Samarin ◽  
Viktor V. Slavsky ◽  
Maria V. Kurkina
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Data Processing ◽  
Digital Image ◽  
Digital Images ◽  
Remote Sensing Data ◽  
Complete Information ◽  
Contour Lines ◽  
Topographic Characteristics ◽  
Topological Characteristics

The paper describes the practical results received from digital images topographic characteristics calculating in Matlab, such as length and curvature of contour lines, density of lengths and curvature, as well as irregularities of contour lines of the first and second order. Topological characteristics contain complete information about the shape and contours of a digital image, which allows them to be effectively used in solving the problems of remote sensing data processing, analysis of biomedical images, classification and pattern recognition problems.

Improved t-SNE based manifold dimensional reduction for remote sensing data processing

2018 ◽  
Vol 78 (4) ◽  
pp. 4311-4326 ◽  
Author(s):  
Weijing Song ◽  
Lizhe Wang ◽  
Peng Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Dimensional Reduction ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

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