An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning

Abstract

Object-Based Image Analysis (OBIA) is a method employed in the field of remote sensing with the objective of enhancing classification accuracy. This is achieved by focusing on image segments comprising groups of pixels, rather than evaluating individual pixels. By addressing the limitations of traditional pixel-based methods, OBIA is employed for the classification of segments based on their attributes. The present study evaluates the use of OBIA-based classification in conjunction with deep learning and machine learning classifiers. A study area, approximately 210 km2 located in Ankara, was selected and SPOT-6 imagery with a spatial resolution of 1.5 meters and 4 spectral bands (red, green, blue and near infrared) was employed for this purpose. In the segmentation stage, a multiresolution segmentation approach was employed, and classification process was conducted using a Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost). The CNN classifier demonstrated superior performance compared to the XGBoost algorithm, with an improvement of 2.7%. The Shapley Additive Explanations (SHAP) technique, an effective Explainable Artificial Intelligence (XAI) method, was employed to assess the explainability of the classifiers. The SHAP analysis indicated that the HSI transform was the most influential factor in the XGBoost algorithm’s decision-making process whereas the average DN values of the green band were the most effective feature for the CNN model. Global SHAP analyses elucidated the overarching model decision-making process, whereas class-specific analyses furnished insights into the classification of each land use and land cover (LULC) class. © 2025 Elsevier B.V., All rights reserved.