A Lightweight Relu-Based Feature Fusion For Aerial Scene Classification

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.

MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning

“…As a universal feature is shared for all downstream tasks, it is computationally efficient but raises a privacy concern while sharing with outside agents due to offering a common feature for all tasks. Similar behavior patterns can be found in other recent literature [2,3] where features from multiple layers of deep models are fused to form the universal features and image classification task is accomplished.…”

Classification of Scenes in Aerial Images with Deep Learning Models

“…For this reason, researchers in the literature have initially created benchmark data sets for artificial intelligence-based vision systems to be developed in this field [1][2][3][4][5]. Deep learning-based methods were developed on these datasets [6][7][8][9][10][11][12]. Deep learning is a sub-branch of artificial intelligence and first attracted attention with the ImageNet competition in 2012.…”

“…The proposed method achieved an overall accuracy of 83%, an F1 score of 0.797, and classified 15 of the classes with 95% or higher accuracy. Study [10] proposes a transfer learning based technique for aerial scene classification using a layer selection strategy called ReLu Based Feature Fusion (RBFF). In RBFF, a pre-trained MobileNetV2 deep learning model is used for feature discovery.…”