Deep Residual SVM: A Hybrid Learning Approach to obtain High Discriminative Feature for Land Use and Land Cover Classification

Abstract

Classification of multispectral images is impacted by challenges such as inadequate training samples, limited ground truth, and complex spatiotemporal dependencies. The accuracy of classifiers due to the lack of training samples is also compounded by the class imbalance problem in spatial analytics.This paper proposes a novel idea of exploiting the deep residual networks trained on millions of images for high discriminative feature representations. These derived representations are classified using bias-corrected Adam optimizer-based Support Vector Machine classifiers. The intuition behind this approach is the significance of representations in the last layers of the pre-trained network that contributes to the classification accuracy. These encapsulated representations from the pre-trained network can outperform traditional feature representations. Residual networks solved the vanishing gradients problem in deep learning and improved classification accuracy. Hence, this work explores the combined potential of the representation power of deep residual pre-trained networks and the classification ability of Support Vector Machines. The model implementation experiments have been conducted using two publicly available benchmark datasets, Eurosat and UC Merced for Land Use and Land Cover Classification. The proposed model DRSVM (Deep Residual Support Vector Machines) demonstrated higher efficiency in terms of computational parameters and time complexity than conventional convolutional neural networks, pre-trained networks, and Support Vector Machines with comparable accuracy. Data augmentation techniques are used further for enhancing the performance of the model. Conventional supervised deep learning algorithms like CNN overfit in dense layers in case of inadequate training data. This hybrid approach alleviates overfitting issues by eliminating dense layers and replacing the Softmax function with Support Vector Machines for classification.