On multi-class aerial image classification using learning machines
Qurban Ali Memon, Najiya Koderi Valappil · 2021
Chapter Contents: Abstract 15.1 Introduction 15.2 Learning approaches 15.2.1 Deep learning networks 15.2.1.1 Recursive neural network 15.2.1.2 Recurrent neural network 15.2.1.3 Convolutional neural network 15.2.1.4 Deep generative networks 15.2.2 Feature learning 15.2.3 Challenges for deep learning 15.2.4 Challenges related to aerial video classification 15.2.5 Applications 15.3 Learning architecture and classification 15.3.1 Supervised learning architectures 15.3.2 Unsupervised learning 15.3.3 Deep learning for planning and situational awareness 15.3.4 Deep learning for motion control 15.3.5 Object detection 15.3.6 Classification 15.3.6.1 Binary classification 15.3.6.2 Multi-class classification 15.4 Training 15.4.1 Weight initialization 15.4.2 Convolutional methods 15.4.3 Activation functions 15.4.4 Subsampling or pooling layer 15.4.5 Optimization techniques 15.4.6 Benchmark datasets 15.5 Energy efficiency in learning approaches 15.6 Performance metrics 15.7 Development kits and frameworks 15.8 Discussions and future directions References