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


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