Classification of mangroves vegetation species using texture analysis on Rapideye satellite imagery

2013 ◽  
Author(s):  
M. A. Roslani ◽  
M. A. Mustapha ◽  
T. Lihan ◽  
W. A. Wan Juliana
Keyword(s):  
Texture Analysis ◽  
Satellite Imagery ◽  
Vegetation Species

scholarly journals Multifeature texture analysis for the classification of clouds in satellite imagery

2003 ◽  
Vol 41 (11) ◽  
pp. 2662-2668 ◽  
Author(s):  
C.I. Christodoulou ◽  
S.C. Michaelides ◽  
C.S. Pattichis
Keyword(s):  
Texture Analysis ◽  
Satellite Imagery

scholarly journals 3D texture analysis for classification of second harmonic generation images of human ovarian cancer

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Bruce Wen ◽  
Kirby R. Campbell ◽  
Karissa Tilbury ◽  
Oleg Nadiarnykh ◽  
Molly A. Brewer ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Second Harmonic Generation ◽  
Texture Analysis ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Human Ovarian Cancer ◽  
3D Texture ◽  
3D Texture Analysis

Application of evolutionary techniques to temporal classification of cloud systems using satellite imagery

1998 ◽  
Author(s):  
Jo Ann Parikh ◽  
John S. DaPonte ◽  
Joseph N. Vitale ◽  
George Tselioudis
Keyword(s):  
Satellite Imagery ◽  
Cloud Systems

scholarly journals Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land

2013 ◽  
Vol 46 (6) ◽  
pp. 426-433 ◽  
Author(s):  
Kyung-Do Lee ◽  
Shin-Chul Baek ◽  
Suk-Young Hong ◽  
Yi-Hyun Kim ◽  
Sang-Il Na ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Reclaimed Land ◽  
Soil Desalination ◽  
High Resolution Satellite Imagery

scholarly journals Mechanical Fault Classification of an Induction Motor using Texture Analysis

2013 ◽  
Vol 18 (12) ◽  
pp. 11-19 ◽  
Author(s):  
Won-Chul Jang ◽  
Yong-Hoon Park ◽  
Myeong-Su Kang ◽  
Jong-Myon Kim
Keyword(s):  
Induction Motor ◽  
Texture Analysis ◽  
Fault Classification

scholarly journals CLASSIFICATION OF THE STRUCTURE OF CITIES THROUGH MID-RESOLUTION SATELLITE IMAGERY AND PATCH BASED NEURAL NETWORKS

2018 ◽  
Vol XLII-5 ◽  
pp. 713-717
Author(s):  
D. Verma ◽  
A. Jana ◽  
K. Ramamritham
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Visual Exploration ◽  
Urban Spatial Structure ◽  
Urban Forms ◽  
Medium Resolution ◽  
Convolutional Autoencoder ◽  
Energy Consumption Patterns ◽  
Advanced Computer

<p><strong>Abstract.</strong> The studies in the classification of the urban spatial structure have been essential in deriving insights into the land cover and the built typology which helped in the estimation of energy consumption patterns, urban density, compactness, and hierarchy of settlements. However, the analysis and comparison of the physical forms of the cities have been attempted in a piecemeal fashion where the requirement of datasets and the computation power for analysis has been a major hindrance. With the advancement in machine learning based techniques, large datasets such as satellite imagery can be studied with advanced computer vision methods. These solutions may help in studying the intricate nature of human habitats in large extents of geographical areas including various urban areas. This study utilizes smaller patches of medium resolution Sentinel-2B Imagery of ten different cities in India to explore the urban forms present in these cities. This study uses Stacked Convolutional Autoencoder (CAE) to reduce the dimensionality of satellite imagery patches and unsupervised clustering techniques such as t-SNE and K-means to study the characteristics of similar patches. On analyzing the clusters through visual exploration, similar patches are delineated and provided with corresponding labels representing urban forms. Individual clusters are then studied with respect to each city. The motive of the study is to gain insights into the different types of morphological patterns present within and among cities.</p>

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