Deep neural network models for hyperspectral images

Abstract With the developments of remote sensing technologies, hyperspectral images (HSIs) captured by hyperspectral imaging sensors have successfully been used to detect and classify objects. Hyperspectral images contain abundant spatial and spectral information. Information extraction methods for panchromatic or multispectral images are not suitable for hyperspectral image processing. Therefore, new information extraction models and methods need to be developed according to the mechanism of hyperspectral remote sensing and the characteristics of images. A new hyperspectral image classification (HSIC) framework-depth multiscale spatial spectral feature extraction algorithm is introduced, focusing on the effective identification features of HSIC. Then an unsupervised feature learning method for HSI classification, which is based on a recursive autoencoders (RAEs) network is introduced. Finally, a superpixel-based multiple local convolution neural network (SML-CNN) model for panchromatic and MS images classification is described.

• Publication year

  2020

• Venue

  Unknown venue

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/b978-0-12-819795-0.00022-0
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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