Support Vector Machine for Classification of Hyperspectral Remote Sensing Imagery

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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A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v11i4.4564 ◽

2015 ◽

Vol 11 (4) ◽

pp. 14 ◽

Cited By ~ 1

Author(s):

Pan Xin ◽

Hongbin Sun

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Land Cover ◽

Sample Selection ◽

Region Of Interest ◽

Computation Time ◽

Support Vector ◽

Machine Model ◽

Remote Sensing Imagery ◽

Remote Sensing Classification

Advancements in remote sensing technology have led to improvements in the acquisition of land cover information. The extraction of accurate and timely knowledge about land cover from remote sensing imagery largely depends on the classification techniques used. Support vector machine has been receiving considerable attention as a promising method for classifying remote sensing imagery. However, the support vector machine learning process typically requires a large memory and significant computation time for treating a large sample set, in which some of the samples might be redundant and useless for the support vector machine model training. Therefore, higher-quality and fewer samples from the sample selection should be utilized for support vector machine-based remote sensing classification. A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work. A Landsat-5 Thematic Mapper imagery acquired on August 31, 2009 (orbit number 113/27) is adopted in our experiments. The study area's land cover/use was divided into five categories. Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels. Results show that for most cases, our method can achieve higher classification accuracy than random sample selection method.

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Classification of Urban Hyperspectral Remote Sensing Imagery Based on Optimized Spectral Angle Mapping

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-018-0929-1 ◽

2019 ◽

Vol 47 (2) ◽

pp. 289-294 ◽

Cited By ~ 4

Author(s):

Yu Liu ◽

Shan Lu ◽

Xingtong Lu ◽

Zheyi Wang ◽

Chun Chen ◽

...

Keyword(s):

Remote Sensing ◽

Hyperspectral Remote Sensing ◽

Remote Sensing Imagery ◽

Spectral Angle

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