Home > Published Issues > 2024 > Volume 15, No. 3, 2024 >
JAIT 2024 Vol.15(3): 422-434
doi: 10.12720/jait.15.3.422-434

Object Classification by Effective Segmentation of Tree Canopy Using U-Net Model

S. Vasavi *, Atluri Lakshmi Likhitha, Veeranki Sai Premchand, and Jampa Yasaswini
Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
Email: vasavi.movva@gmail.com (V.S.); a.lakshmilikhitha@gmail.com (A.L.L.); 208w1a0556@vrsec.ac.in (V.S.P.); 208w1a0525@vrsec.ac.in (J.Y.)
*Corresponding author

Manuscript received June 6, 2023; revised July 30, 2023; accepted September 4, 2023; published March 26, 2024.

Abstract—According to the Forest Survey of India and yearly report, Kerala has a total area covered by trees of 2951 sq km, which is 7.59% of the state’s total area. In regions like Kerala, identifying objects from Very High-Resolution Satellite (VHRS) images has become a major challenge. Using a method known as “tree canopy”, which refers to the area shaded by trees, objects that are covered by trees can be identified. For mapping tree crowns Mask Region-based Convolutional Neural Network (R-CNN) is used. It struggles for accurate segmentation and distinguish objects from the surrounding trees, leading to misclassification and incorrect object masks. It can also be computationally expensive, making it challenging to process high-resolution images in real-time. A deep learning model that uses a semantic segmentation approach is proposed to detect tree canopy covering objects. Dataset with 0.5 m resolution images is prepared from SAS Planet images. The input image is now preprocessed using pre-processing techniques and trained with U-Net. Further, the images are closed using morphological operation to detect the object. The model is evaluated for 25 epochs with an accuracy of 92%. Finally, the objects are classified based on semantic segmentation using U-Net with backbone of ResNet34. The objects are classified as buildings, roads, water bodies and got accuracy of 84%.
Keywords—tree canopy, Very High-Resolution Satellite (VHRS) images, object detection, U-Net, semantic segmentation, ResNet34, object classification

Cite: S. Vasavi, Atluri Lakshmi Likhitha, Veeranki Sai Premchand, and Jampa Yasaswini , "Object Classification by Effective Segmentation of Tree Canopy Using U-Net Model," Journal of Advances in Information Technology, Vol. 15, No. 3, pp. 422-434, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.