Sequential PCA-based classification of mediterranean forest plants using airborne hyperspectral remote sensing

Alon Dadon*, Moshe Mandelmilch, Eyal Ben-Dor, Efrat Sheffer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.

Original languageEnglish
Article number2800
JournalRemote Sensing
Issue number23
StatePublished - 1 Dec 2019


  • Hyperspectral remote sensing
  • Mediterranean forest
  • Plant species
  • Principal component analysis
  • Unsupervised classification


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