The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A new deep convolutional neural network for fast hyperspectral image classification
Open accessAbstract
Abstract Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention in this field. CNNs have proved to be very effective in areas such as image recognition and classification, especially for the classification of large sets composed by two-dimensional images. However, their application to multispectral and hyperspectral images faces some challenges, especially related to the processing of the high-dimensional information contained in multidimensional data cubes. This results in a significant increase in computation time. In this paper, we present a new CNN architecture for the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both spectral and spatial information. It also implements a border mirroring strategy to effectively process border areas in the image, and has been efficiently implemented using graphics processing units (GPUs). Our experimental results indicate that the proposed network performs accurately and efficiently, achieving a reduction of the computation time and increasing the accuracy in the classification of hyperspectral images when compared to other traditional ANN techniques.
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Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
Deep support vector machine for hyperspectral image classification
Okwuashi, O., & Ndehedehe, C. E. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107298 · Cited by 283
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning
Liu, S., Shi, Q., & Zhang, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3018879 · Cited by 268
Ding, P., Zhang, Y., Deng, W.-J., Jia, P., & Kuijper, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.05.005 · Cited by 174
Metadata
- Authors
- Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 120-147
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2772452219
- Cited by
- 607
- References
- 11
- FWCI
- 45.01
- Type
- journal article
- Language
- en
- Open access
- green
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