OVERVIEW OF APPLICATIONS AND METHODS OF REMOTE SENSING DATA CLASSIFICATION

Abstract

  In this comprehensive study, we explore the multifaceted challenges and limitations associated with classification methods in machine learning, particularly focusing on image classification. Despite the remarkable achievements of machine learning models in this domain, there is a tendency to prioritize accuracy while overlooking other critical factors such as data preparation, the impact of noise on accuracy, computational power requirements, and more. A significant portion of the study addresses the issues of model interpretability and the inherent complexities of deep learning models, often referred to as "black box" models. These models, while highly accurate, pose significant challenges in terms of understanding and explaining their decision-making processes. This is particularly problematic in high-stakes environments such as healthcare and judicial systems, where decisions can have profound implications. Adversarial attacks represent another critical challenge discussed in this study. These attacks involve manipulating input data to deceive models into making incorrect predictions. We highlight the need for robust optimization techniques in decision tree thresholds to minimize potential losses in worst-case scenarios of data perturbations. Furthermore, the study delves into the computational demands of training deep learning models, emphasizing the environmental and economic constraints posed by increasing computational loads. The research suggests a shift towards more computationally efficient methods to mitigate these challenges without compromising performance. Additionally, the study discusses the challenges of applying machine learning in scenarios where only a limited number of labeled examples are available. Neural networks require large volumes of labeled data to perform effectively, a condition not always feasible, especially in specialized fields with limited data availability. This limitation necessitates the development of alternative strategies that can learn effectively from smaller data sets. In conclusion, while machine learning, particularly deep learning, continues to advance and achieve high accuracy in image classification, this study underscores the importance of addressing interpretability, adversarial robustness, computational efficiency, and the ability to perform under constrained data conditions. Developing models that are not only accurate but also interpretable, less resource-intensive, and capable of learning from limited data could lead to more sustainable and ethically responsible applications of machine learning technologies.