The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods

Geocarto International, cilt.36, sa.7, ss.758-772, 2021 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 36 Sayı: 7
Basım Tarihi: 2021
Dergi Adı: Geocarto International
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Environment Index, Geobase, INSPEC
Sayfa Sayıları: ss.758-772
Yıldız Teknik Üniversitesi Adresli: Evet

Rize district is an important tea production site in Turkey, which

is known for high quality tea. Determining the temporal changes

is very crucial from the viewpoint of agricultural management

and protection of tea areas. In addition, delineation of tea gardens

using photogrammetric evaluation techniques for a single

orthoimage takes approximately 8 h of labour work, which is both

costly and time-consuming process. To overcome these issues, a

method is proposed for demarcation of tea gardens from highresolution

orthoimages. In this article, a hierarchical object-based

segmentation using mean-shift (MS) and supervised machine

learning (ML) methods are investigated for delineation of tea gardens.

First, the MS algorithm was applied to partition the images

into homogeneous segments (objects) and then from each segment,

various spectral, spatial and textural features were

extracted. Finally, four most widely used supervised ML classifiers,

support vector machine (SVM), artificial neural network (ANN),

random forest (RF), and decision trees (DTs), were selected for

classification of objects into tea gardens and other types of trees.

Photogrammetrically evaluated tea garden borders were taken as

reference data to evaluate the performance of the proposed

methods. The experiments showed that all selected supervised

classifiers were effective for delineation of the tea gardens from

high-resolution images.