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.
