Object Detection in Hyperspectral Images

Longbin Yan, Min Zhao, Xiuheng Wang, Yuge Zhang, Jie Chen

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

High spectral resolution of hyperspectral images allows the detection and classification of materials in the observed images. However, existing research on hyperspectral detection mainly focuses on pixel-level study, partially due to the low spatial resolution in typical earth observation applications. With the development of imaging techniques, high-spatial-resolution hyperspectral data can be obtained and object-level detection is necessary for many applications. In this work, the object-based hyperspectral detection problem is formulated, and a convolutional neural network is then designed based on the specific characteristics of this problem. Moreover, a hyperspectral dataset with over 400 high-quality images for object-level target detection is created. Experimental results validate the proposed framework and show its superior performance.

Original languageEnglish
Article number9354961
Pages (from-to)508-512
Number of pages5
JournalIEEE Signal Processing Letters
Volume28
DOIs
StatePublished - 2021

Keywords

  • Hyperspectral imaging
  • convolutional neural network
  • object-based detection
  • target detection

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