Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation

Xudong Zhao, Mengmeng Zhang, Ran Tao*, Wei Li, Wenzhi Liao, Wilfried Philips

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Limitation of labeled samples has always been a challenge for hyperspectral image (HSI) classification. In real remote sensing applications, we encounter a situation where an HSI scene is not labeled at all. To solve this problem, cross-domain learning methods are developed by utilizing another HSI scene with similar land covers and sufficient labeled samples. However, the disparity between HSI scenes is still a challenge in reducing the classification performance, which may be affected by variations in illumination and weather. As a robust supplement to these variations, light detection and ranging (LiDAR) data provide stable elevation and spatial information. In this article, we propose a multisource cross-domain classification method using fractional fusion and spatial-spectral domain adaptation to reduce the disparity between scenes. First, the spatial information of HSI is preserved by fractional differential masks. Then, the LiDAR data are utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed to reduce domain shift at the feature level and extract discriminative spatial-spectral features. Experimental results on HSI and LiDAR scenes show 5% -10% improvements in overall accuracy compared with the state-of-the-art methods.

Original languageEnglish
Pages (from-to)5721-5733
Number of pages13
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume15
DOIs
Publication statusPublished - 2022

Keywords

  • Cross-domain classification
  • fractional fusion (FrF)
  • hyperspectral image (HSI)
  • light detection and ranging (LiDAR)
  • spatial-spectral domain adaptation (SSDA)

Fingerprint

Dive into the research topics of 'Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation'. Together they form a unique fingerprint.

Cite this