Multi-Scale Mahalanobis Kernel-Based Support Vector Machine for Classification of High-Resolution Remote Sensing Images

2019 ◽  
Author(s):  
Genyun Sun ◽  
Xueqian Rong ◽  
Aizhu Zhang ◽  
Hui Huang ◽  
Jun Rong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
High Resolution ◽  
Support Vector ◽  
Remote Sensing Images ◽  
Multi Scale

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.

scholarly journals MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 247-255 ◽  
Author(s):  
Y. Di ◽  
G. Jiang ◽  
L. Yan ◽  
H. Liu ◽  
S. Zheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Minimum Spanning Tree ◽  
Network Evolution ◽  
Edge Weight ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Multiple Features ◽  
Multi Scale ◽  
Initial Segmentation

Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.

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