V3DI ensemble model for high-accuracy aerial scene classification
Year: 2025
Type: article
Abstract: Aerial images are valuable for observing land, allowing detailed examination of Earth's surface features. As remote sensing (RS) imagery becomes more abundant, there is a growing need to fully utilize these images for smarter Earth observation. Understanding large and complex RS images is crucial. Satellite image scenery categorization, which involves labeling images based on their content, has diverse applications. Deep Learning (DL), using neural networks' powerful attribute learning capabilities, has made significant strides in categorizing satellite imagery scenes. However, recent advances in DL for scenery categorization of RS images are lacking. In our study, we employed three transfer learning (TL) models - VGG16, Densenet201 (D-201), and InceptionV3(IV3) - for classifying aerial images. VGG16 achieved 94% accuracy, while D-201 and IV3 reached 97% accuracy. Combining these models into an ensemble (V3DI ensemble model) improved accuracy to an impressive 99%. This ensemble model combines individual models' classification decisions using majority voting. We demonstrate the efficiency of this approach by showing how ensemble classification accuracy surpasses that of training individual models. Additionally, we preprocess the dataset with a Gabor filter for edge enhancement and denoising to enhance the model's overall performance.
Source: Machine Graphics and Vision
Authors: K Aditya Shastry, Reshma Itagi
Institution: Nitte Meenakshi Institute of Technology
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408909379