Jun 3, 2019 · Abstract: Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 1. Dynamic Multicontext Segmentation of Remote. Sensing Images Based on Convolutional Networks. Keiller ...
Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a ...
A novel technique to perform semantic segmentation of remote sensing images that exploits a multicontext paradigm without increasing the number of ...
Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks. K Nogueira, M Dalla Mura, J Chanussot, WR Schwartz, JA Dos Santos.
Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks. K Nogueira, M Dalla Mura, J Chanussot, WR Schwartz, JA Dos Santos.
Fully Convolutional Network (FCN) was one of the first deep learning-based techniques proposed to perform semantic segmentation. This network extracts features ...
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Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial ...
In this paper, a comprehensive review is performed on remote sensing survey systems and various kinds of specially designed deep learning architectures.
Nov 13, 2022 · We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding.