Hyperspectral Image Classification With Deep Learning Models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Xiaofei Yang
  • Yunming Ye
  • Xutao Li
  • Xiaofeng Zhang
  • Xiaohui Huang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)5408-5423
Journal / PublicationIEEE Transactions on Geoscience and Remote Sensing
Volume56
Issue number9
Online published17 Apr 2018
Publication statusPublished - Sep 2018

Abstract

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Research Area(s)

  • Context modeling, Convolution, Convolutional neural network (CNN), deep learning, hyperspectral image, Hyperspectral imaging, Kernel, Machine learning, Task analysis

Citation Format(s)

Hyperspectral Image Classification With Deep Learning Models. / Yang, Xiaofei; Ye, Yunming; Li, Xutao; Lau, Raymond Y. K.; Zhang, Xiaofeng; Huang, Xiaohui.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 9, 09.2018, p. 5408-5423.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review