A fast instance selection method for support vector machines in building extraction

Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is ref...

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Vydáno v:Applied soft computing Ročník 97; číslo B; s. 106716
Hlavní autoři: Aslani, Mohammad, Seipel, Stefan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2020
Témata:
Big data
Building extraction
Computerized Image Processing
Data reduction
Datoriserad bildbehandling
Instance selection
Support vector machines
Support vector machines
Instance selection
Big data
Data reduction
Building extraction
ISSN:1568-4946, 1872-9681, 1872-9681
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Shrnutí:Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH. •The linear time complexity of DR.LSH makes it suitable for handling big datasets.•DR.LSH is competitive with other state-of-the-art methods in building extraction.•DR.LSH can significantly reduce the number of instances and execution time.
ISSN:1568-4946
1872-9681
1872-9681
DOI:10.1016/j.asoc.2020.106716