Continuous Moment-Based Features for Classification of Ground Vehicle SAR Images

2016 ◽  
Author(s):  
Pouya Bolourchi ◽  
Hasan Demirel ◽  
Sener Uysal
Keyword(s):  
Sar Images ◽  
Ground Vehicle

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

scholarly journals Markovian classification of SAR images using $\mathcal{G}^{0}_{I}$ model

2009 ◽  
Vol 23 (2) ◽  
pp. 166-178 ◽  
Author(s):  
Mery Picco ◽  
Gabriela Palacio
Keyword(s):  
Sar Images

Experience gained with texture modeling and classification of 1 meter resolution SAR images

2009 ◽  
Author(s):  
Daniela Espinoza-Molina ◽  
Gottfried Schwarz ◽  
Mihai Datcu
Keyword(s):  
Sar Images ◽  
Texture Modeling

An end-user-oriented framework for the classification of multitemporal SAR images

2016 ◽  
Vol 37 (1) ◽  
pp. 248-261 ◽  
Author(s):  
D. Amitrano ◽  
G. Di Martino ◽  
A. Iodice ◽  
D. Riccio ◽  
G. Ruello
Keyword(s):  
End User ◽  
Sar Images
Sign in / Sign up

Export Citation Format

Share Document