Tree Detection from Very High Spatial Resolution RGB Satellite Imagery Using Deep Learning

Sukkar A., TÜRKER M.

1st International conference on Mediterranean Geosciences Union, MedGU 2021, İstanbul, Türkiye, 25 - 28 Kasım 2021, ss.145-149 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1007/978-3-031-43218-7_34
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.145-149
  • Anahtar Kelimeler: Deep learning, RGB satellite images, Tree detection, U-Net, Vegetation index
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Tree detection from space imagery is important in the agriculture and forestry industries. However, very high spatial resolution satellite imagery represents fine details on the ground making the object detection task more challenging. Most of the existing tree detection methods use multispectral bands, including an infrared (IR) band, which provides distinct information about vegetation areas making the detection task more manageable. However, the sheer amount of optical data employed as input to information extraction procedures contains only three bands, and IR bands may not always be available. Thus, this study presents automatic tree detection by only using the Red, Green, and Blue (RGB) bands of very high spatial resolution satellite images through deep learning. The proposed method was built on top of a U-Net architecture whose ability to detect different types of trees is explored. The U-Net architecture was trained using WorldView-3 RGB images. In addition to RGB bands, vegetation indices were computed and used as additional bands to investigate their effects on the results. In this respect, six models were generated, and each model was trained and tested individually. The models used include (RGB, RGB + VARI, RGB + GLI, RGB + GRVI, RGB + all indices, and only vegetation indices). Four accuracy assessment equations were calculated for each model, and the results were compared.