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SVM or deep learning? A comparative study on remote sensing image classification
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DOI：10.1007/s00500-016-2247-2.png)
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Abstract
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With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.
Keywords:
Spatial big data
Sparse auto-encoder
Support vector machine
Active learning
Remote sensing
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