Integrated Learning and Feature Selection for Deep Neural Networks in Multispectral Images

Anthony Ortiz, Alonso Granados, Olac Fuentes, Christopher Kiekintveld, Dalton S. Rosario, Zachary Bell

Abstract: The curse of dimensionality is a well-known phenomenon that arises when applying machine learning algorithms to highly-dimensional data; it degrades performance as a function of increasing dimension. Due to the high data dimensionality of multispectral and hyperspectral imagery, classifiers trained on limited samples with many spectral bands tend to overfit, leading to weak generalization capability. In this work, we propose an end-to-end framework to effectively integrate input feature selection into the training procedure of a deep neural network for dimensionality reduction. We show that Integrated Learning and Feature Selection (ILFS) significantly improves performance on neural networks for multispectral imagery applications. We also evaluate the proposed methodology as a potential defense against adversarial examples, which are malicious inputs carefully designed to fool a machine learning system. Our experimental results show that methods for generating adversarial examples designed for RGB space are also effective for multispectral imagery and that ILFS significantly mitigates their effect.