Visual Question Answering for Wishart H-Alpha Classification of Polarimetric SAR Images

Polarimetric Synthetic Aperture Radar (PolSAR) images offer a rich repository of information, crucial for diverse applications ranging from classification to target identification. In the domain of PolSAR image classification, the Wishart classifier emerged as a prominent and widely employed technique. This classifier is often used to articulate the properties of polarimetric scattering types in images, providing valuable insight into various types of targets. With the growing interest in multidisciplinary Artificial Intelligence (AI) research, especially in computer vision and Natural Language Processing (NLP), our goal is to integrate this enthusiasm into polarimetric image analysis. We propose extending the Wishart classifier framework to include a free-form and open-ended Visual Question Answering (VQA) model. This model is designed to answer natural language questions related to PolSAR images, covering pixel details and scattering patterns. The objective is to provide accurate natural language responses that reflect real-world scenarios, such as assisting the visually impaired. Both questions and answers in this context are intentionally left open-ended to capture the complexity of inquiries in the polarimetric SAR images domain.