A Lightweight Aerial Scene Classifier Based on Adaptive Fusion of MobileNet and CapsNet

Scene-level classification of aerial images is challenging due to varying object scales, positions, inter-class similarity, and intra-class diversity. Convolutional Neural Networks (CNNs) are effective for extracting useful feature maps, while Capsule Networks (CapsNets) are adept at recognizing the pose information of objects in an image. This work uses both models to ensure that aerial scenes are accurately classified. Additionally, considering that CapsNets typically require many parameters and floating-point operations, we aim to make our model lightweight. Using adaptive fusion of features extracted from MobileNetv2, a lightweight CNN model, enables the capsule network to learn faster, resulting in improved performance. With more than 58% parameter reduction for the datasets UC Merced, AID, and SIRI-WHU, the proposed approach, termed as AdMobiCaps, achieves significantly better performance compared to traditional CapsNet showcasing its potential in aerial scene classification.