Understanding Satellite Image Processing and Black Box Models with Ante-Hoc and Post-Hoc Explanations in Deep Learning

This research aims to investigate the challenges of using black box models in deep learning and analyzing satellite images through a thorough meta-analysis. The main goal of the study is to improve transparency and interpretability, with a focus on providing explanations both before and after model training. Transparency elements are integrated into models during the initial stages to enhance their clarity. Following the completion of training for complex models, explanations are utilized to comprehend how decisions are made. The research focuses on using both of these approaches to tackle the issues linked with deep learning models in satellite image processing. Through the use of design-time and post-modeling methods, AI can be used for analyzing satellite images in a way that is easier to understand and accountable.