Evaluating the effects of texture features on Pinus sylvestris classification using high-resolution aerial imagery

Erdem F., BAYRAK O. C.

Ecological Informatics, vol.78, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 78
  • Publication Date: 2023
  • Doi Number: 10.1016/j.ecoinf.2023.102389
  • Journal Name: Ecological Informatics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, BIOSIS, CAB Abstracts, Geobase, Pollution Abstracts, Veterinary Science Database
  • Keywords: Machine learning, Pinus sylvestris, Remote sensing, Texture features, Tree species classification
  • Hacettepe University Affiliated: Yes


Pinus sylvestris, also known as Scots pine, is the most widespread type of pine tree found around the world. Texture features can recognize patterns in an image's texture and so distinguish different species with similar spectral properties. They also record information about the spatial layout of pixels inside an image. Nonetheless, previous research has not addressed the effects of textural variables on Scots pine classification using high-resolution aerial images. In the context of this investigation, we used TreeSatAI, a benchmark dataset with categorized high-resolution aerial imagery, to conduct a comparative analysis, and we examined a comprehensive set of 93 texture features (radiomics) by generating several parameters such as First Order, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). We proceeded to train machine learning models utilizing the state-of-the-art algorithms such as XGBoost, LightGBM, and Gradient Boosting Machines (GBM). Upon conducting a more comprehensive analysis, it has come to light that the GLDM and GLRLM features have exhibited greater significance in the realm of satellite image processing research, specifically pertaining to the precise classification of Scots pine. This finding challenges the conventional reliance on GLCM features, which have traditionally been favored in similar studies. This study is remarkable due to its emphasis on the efficacy of employing high-resolution aerial imagery and texture features within the realm of satellite image processing applications.