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 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>

Inclusion of topographic variables in an unsupervised classification of satellite imagery

2004 ◽  
Vol 30 (2) ◽  
pp. 137-149 ◽  
Author(s):  
Michael A Wulder ◽  
Steven E Franklin ◽  
Joanne C White ◽  
Morgan M Cranny ◽  
Jeff A Dechka
Keyword(s):  
Satellite Imagery ◽  
Unsupervised Classification

Detection by classification of buildings in multispectral satellite imagery

2016 ◽  
Author(s):  
Tomohiro Ishii ◽  
Edgar Simo-Serra ◽  
Satoshi Iizuka ◽  
Yoshihiko Mochizuki ◽  
Akihiro Sugimoto ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Multispectral Satellite Imagery

scholarly journals A comparison of supraglacial lake observations derived from MODIS imagery at the western margin of the Greenland ice sheet

2013 ◽  
Vol 59 (218) ◽  
pp. 1179-1188 ◽  
Author(s):  
Amber A. Leeson ◽  
Andrew Shepherd ◽  
Aud V. Sundal ◽  
A. Malin Johansson ◽  
Nick Selmes ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Automated Classification ◽  
Temporal Sampling ◽  
Supraglacial Lakes ◽  
Modis Imagery ◽  
Single Dataset ◽  
Average Size

AbstractSupraglacial lakes (SGLs) affect the dynamics of the Greenland ice sheet by storing runoff and draining episodically. We investigate the evolution of SGLs as reported in three datasets, each based on automated classification of satellite imagery. Although the datasets span the period 2001–10, there are differences in temporal sampling, and only the years 2005–07 are common. By subsampling the most populous dataset, we recommend a sampling frequency of one image per 6.5 days in order to minimize uncertainty associated with poor temporal sampling. When compared with manual classification of satellite imagery, all three datasets are found to omit a sizeable (29, 48 and 41 %) fraction of lakes and are estimated to document the average size of SGLs to within 0.78, 0.48 and 0.95 km2. We combine the datasets using a hierarchical scheme, producing a single, optimized, dataset. This combined record reports up to 67% more lakes than a single dataset. During 2005–07, the rate of SGL growth tends to follow the rate at which runoff increases in each year. In 2007, lakes drain earlier than in 2005 and 2006 and remain absent despite continued runoff. This suggests that lakes continue to act as open surface–bed conduits following drainage.

scholarly journals Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes

2013 ◽  
Vol 40 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Carlos H. Wachholz de Souza ◽  
Erivelto Mercante ◽  
Victor H. R. Prudente ◽  
Diego D.D. Justina
Keyword(s):  
Performance Evaluation ◽  
Land Cover ◽  
Satellite Imagery ◽  
Supervised Classification
Sign in / Sign up

Export Citation Format

Share Document