  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.
