A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification

TL;DR

This paper introduces a cost-effective, lightweight feature fusion method using ReLU-based layer selection from MobileNetV2, significantly improving aerial scene classification accuracy with minimal training.

Contribution

The paper presents a novel ReLU-Based Feature Fusion (RBFF) technique that leverages pretrained CNN features for efficient aerial scene classification without retraining the base model.

Findings

RBFF outperforms recent models in accuracy on multiple datasets.

The method is highly cost-effective due to minimal training requirements.

Lightweight model achieves high accuracy with low computational cost.

Abstract

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.