Aerial Scene Classification using VGG16 and Multiclass Linear SVM

Abstract

Aerial scene classification is the process of categorizing and analyzing im ages captured from an aerial perspective, enabling the identification of land cover, objects, and scene composition for various applications. Aerial scene classification plays a crucial role in various fields, including urban planning, environmental monitoring, and disaster management, by providing valuable insights into land cover, objects, and scene composition from an aerial per spective. Accurate classification of aerial scenes enables effective decision making, resource allocation, and informed analysis of large-scale imagery, contributing to improved spatial understanding and efficient management of diverse landscapes. In this work, classification of aerial images using com bination of VGG16 and Multiclass Linear SVM classifier is proposed. Deep Features are extracted using VGG16 and Multiclass Linear SVM classifier is using to classify the given objects. The preprocessed steps include data aug mentation, data normalization, feature extraction using a VGG16 model, and training a multiclass linear SVM classifier for aerial scene classification. The experiments are conducted on NWPU and UCM dataset and performance is evaluated using confusion matrix,precision and recall.The experimental result shows the proposed method yield 90% accuracy for NWPU dataset and 95% accuracy for UCM dataset.