Unsupervised segmentation of LiDAR fused hyperspectral imagery using pointwise mutual information

Torun O., YÜKSEL ERDEM S. E.

INTERNATIONAL JOURNAL OF REMOTE SENSING, cilt.42, sa.17, ss.6465-6480, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 17
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/01431161.2021.1939906
  • Dergi Adı: INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.6465-6480
  • Hacettepe Üniversitesi Adresli: Evet

Özet

In the segmentation of hyperspectral images (HSI), difficulties arise when different objects with similar spectral characteristics are being distinguished. If these objects with similar spectral information have different altitudes, it is possible to partition them based on the elevation information which can be obtained with a light detection and ranging (LiDAR) sensor. In this study, we propose a new affinity matrix to be used in a spectral clustering (SC) framework for the unsupervised segmentation of HSI and LiDAR data. To compose this new affinity matrix, spatial-spectral information obtained from HSI and elevation information obtained from LiDAR are combined using Pointwise Mutual Information (PMI). PMI is a measure of how much one pixel tells about the others in an image. It relies on the fact that the pixels of the same object in the given scene have a higher statistical dependence than the pixels of different objects. Hence, segmenting HSI and LiDAR data using PMI can provide a more comprehensive interpretation of the objects in images. The experimental results on two different real data sets show that the proposed method is very effective for unsupervised segmentation of HSI and LiDAR data and it is much faster when compared to competing spectral clustering algorithms.