Automatic Recognition of Village in Remote Sensing Images by Support Vector Machine Using Co-Occurrence Matrices

2012 ◽  
Vol 10 (1) ◽  
pp. 523-528 ◽  
Author(s):  
Li Li ◽  
Yingyi Chen ◽  
Hongju Gao ◽  
Daoliang Li
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Automatic Recognition ◽  
Support Vector ◽  
Remote Sensing Images

scholarly journals Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework

2020 ◽  
Vol 12 (22) ◽  
pp. 3708 ◽  
Author(s):  
Ziyi Feng ◽  
Guanhua Huang ◽  
Daocai Chi
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Computational Efficiency ◽  
Large Scale ◽  
Statistical Data ◽  
Support Vector ◽  
Small Scale ◽  
Remote Sensing Images ◽  
Learning Machine

Many approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-means clustering algorithm for image segmentation and co-training algorithm to enlarge the sample sets was used to classify the agricultural planting structure at large-scale areas. Data sets collected from a small-scale area within the Hetao Irrigation District (HID) at the upper reaches of the Yellow River basin were used to evaluate the SS-ELM framework. The results of the SS-ELM algorithm were compared with those of the random forest (RF), ELM, support vector machine (SVM) and semi-supervised support vector machine (S-SVM) algorithms. Then the SS-ELM algorithm was applied to analyze the complex planting structure of HID in 1986–2010 by comparing the remote sensing estimated results with the statistical data. In the small-scale case, the SS-ELM algorithm performed better than the RF, ELM, SVM, and S-SVM algorithms. For the SS-ELM algorithm, the average overall accuracy (OA) was in a range of 83.00–92.17%. On the contrary, for the other four algorithms, their average OA values ranged from 56.97% to 92.84%. Whereas, in the classification of planting structure in HID, the SS-ELM algorithm had an excellent performance in classification accuracy and computational efficiency for three major planting crops including maize, wheat, and sunflowers. The estimated areas by using the SS-ELM algorithm based on the remote sensing images were consistent with the statistical data, and their difference was within a range of 3–25%. This implied that the SS-ELM framework could be served as an effective method for the classification of complex planting structures with relatively fast training, good generalization, universal approximation capability, and reasonable learning accuracy.

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