Skip to Main content Skip to Navigation
Conference papers

Parallel Dimensionality-Varied Convolutional Neural Network for Hyperspectral Image Classification

Abstract : Many spectral-spatial classification methods of HSI based on convolutional neural network (CNN) are proposed and achieve outstanding performance recently. However, these methods require tremendous computations with complex network and excessively large model. Moreover, single machine is obviously weak when dealing with big data. In this paper, a parallel dimensionality-varied convolutional neural network (DV-CNN) is proposed to address these issues. The dimensionalities of feature maps extracted vary with stages in DV-CNN, and DV-CNN reduces the dimensionalities of feature maps to simplify the computation and the structure of network without information loss. Besides, the parallel architecture of DV-CNN can obviously reduce the training time. The experiments compared with state-of-the-art methods are performed on Indian Pines and Pavia University scene datasets. The results of experiments demonstrate that parallel DV-CNN can obtain better classification performance, reduce the time consuming and improve the training efficiency.
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download

https://hal.inria.fr/hal-02118826
Contributor : Hal Ifip <>
Submitted on : Friday, May 3, 2019 - 1:26:33 PM
Last modification on : Friday, May 3, 2019 - 3:04:47 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Haicheng Qu, Xiu Yin, Xuejian Liang, Wanjun Liu. Parallel Dimensionality-Varied Convolutional Neural Network for Hyperspectral Image Classification. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.302-309, ⟨10.1007/978-3-030-01313-4_32⟩. ⟨hal-02118826⟩

Share

Metrics

Record views

45