Analyzing Urban Area Land Coverage Using Image Classification Algorithms

2014 ◽  
pp. 439-447
Author(s):  
T. Karthikeyan ◽  
P. Manikandaprabhu
Keyword(s):  
Image Classification ◽  
Urban Area ◽  
Classification Algorithms

scholarly journals Efficient and accurate document image classification algorithms for low-end copy pipelines

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Wen-Hsiung Huang ◽  
Yung-Yao Chen ◽  
Pei-Yu Lin ◽  
Che-Hao Hsu ◽  
Kai-Lung Hua
Keyword(s):  
Image Classification ◽  
Document Image ◽  
Classification Algorithms

Survey of image classification algorithms based on deep learning

2021 ◽  
Author(s):  
Yuxuan Ma ◽  
Bingyang Niu ◽  
Yali Qi
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Classification Algorithms

scholarly journals A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1611
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Google Earth ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Blue Band ◽  
Ground Truth Data ◽  
Water Index ◽  
Water Indices

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

scholarly journals Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches

2017 ◽  
Vol 50 (1) ◽  
pp. 187-201 ◽  
Author(s):  
Amit Kumar Basukala ◽  
Carsten Oldenburg ◽  
Jürgen Schellberg ◽  
Murodjon Sultanov ◽  
Olena Dubovyk
Keyword(s):  
Land Use ◽  
Performance Assessment ◽  
Image Classification ◽  
Classification Algorithms
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