A Perceptually-Informed Approach to Vegetation Identification: Integrating Visual Characteristics and Contextual Information

This study explores the role of visual characteristics in vegetation identification using optical satellite images, with a focus on color information, texture analysis, shape and pattern recognition, and the integration of contextual information. Traditional pixel-based methods relying on isolated spectral signatures have limitations, particularly in complex and heterogeneous landscapes. Recent advances in remote sensing technology have enabled the collection of high-resolution data, providing a wealth of visual information that can be leveraged for more accurate vegetation identification. The study demonstrates the potential of advanced processing techniques, such as Object-based Image Analysis and deep learning models, in extracting and utilizing visual characteristics from high-resolution remote sensing data. These techniques can provide valuable insights into vegetation health, structure, and composition, enabling more informed decision-making. The findings of this study highlight the significance of visual characteristics in vegetation identification and demonstrate the potential of advanced processing techniques in improving land cover classification accuracy. The integration of visual characteristics with contextual information provides a more holistic approach to image analysis, enabling more accurate and robust classifications. This study contributes to the development of more effective and efficient methods for vegetation identification and classification using optical satellite images, and has implications for remote sensing applications in environmental monitoring and management.