Artificial Neural Networks Optimization and Convolution Neural Networks to Classifying Images in Remote Sensing

Abstract

One of important functions of remote sensing data is producing the land-use/land-cover maps. Image classification is one of important applications for remote sensing imaginary. Machine learning (ML) techniques are the most widely used for this purpose in recent years. With the advent of computer vision thus, the need to deal with a large amount of data and avoiding any data redundancy, the deep learning techniques were appeared. Deep learning (DL) is a branch of machine learning that imitates the human brain structure and depends on the artificial neural networks (ANNs). Optimization of the neural networks is necessary for reduce the loss functions and avoiding any redundancy data in the training set, thus raise the accuracy. Genetic algorithms (GA) are the most widely used in the neural networks optimization, which considered as fully connected neural networks. Convolution neural networks (CNNs) are a branch of the artificial neural networks that are saving the computing cost and processing time. Thus, this paper presents a review of the deep learning algorithms specially the artificial neural networks, the genetic algorithm and the convolution neural networks. This paper also introduces a comparative study between the genetic algorithm and the convolution neural networks method. This comparison based on the overall accuracy (OA) and the kappa coefficient. This comparison shows that there are many conditions can affect the classifier accuracy. The results demonstrate that the CNNs algorithms are more accurate than the GA and in the other hand, the CNNs algorithms have lower computing cost.