Publication: Dense semantic labeling of subdecimeter resolution images with convolutional neural networks
Dense semantic labeling of subdecimeter resolution images with convolutional neural networks
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Volpi, M., & Tuia, D. (2017). Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 881–893. https://doi.org/10.1109/tgrs.2016.2616585
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Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm) requires statistical models able to learn high-level concepts from spatial data, with large appearance variations. Convolutional neural networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper, we present a CNN-based system relying on a downsample-thenupsample architecture. Specifically, it first learns a rough spatial map of high-level representations by me
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- Volpi, Michele
- Tuia, Devis
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Volpi, M., & Tuia, D. (2017). Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 881–893. https://doi.org/10.1109/tgrs.2016.2616585
