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arXiv · 1707.05683

Exploiting Convolutional Representations for Multiscale Human Settlement Detection

Dalton Lunga·Dilip Patlolla·Lexie Yang·Jeanette Weaver·Budhendra Bhadhuri
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Abstract

We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

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Dalton Lunga, Dilip Patlolla, Lexie Yang, Jeanette Weaver, Budhendra Bhadhuri. 2017-07-18. Exploiting Convolutional Representations for Multiscale Human Settlement Detection. https://arxiv.org/abs/1707.05683

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