Deep Recurrent Neural Networks for Hyperspectral Image Classification
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) · Technical University of Munich
Abstract
In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown promising results in hyperspectral image classification. Such methodologies, nevertheless, can lead to information loss in representing hyperspectral pixels, which intrinsically have a sequence-based data structure. A recurrent neural network (RNN), an important branch of the deep learning family, is mainly designed to handle sequential data. Can sequence-based RNN be an effective method of hyperspectral image classification? In this paper, we propose a novel RNN model that can effectively analyze hyperspectral pixels as sequential data and then determine…
Citation impact
- FWCI
- 81.72
- Percentile
- 100%
- References
- 52
Authors
3- LMLichao MouCorresponding
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Technical University of Munich
- PGPedram Ghamisi
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Technical University of Munich
- XXXiao Xiang Zhu
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Technical University of Munich
Topics & keywords
- Hyperspectral imaging
- Recurrent neural network
- Computer science
- Artificial intelligence
- Pattern recognition (psychology)
- Deep learning
- Convolutional neural network
- Support vector machine
- Life in Land