Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 150; pp. 115 - 134
• Main Authors: Hossain, Mohammad D., Chen, Dongmei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-04-2019
• Subjects: Geographic object; High spatial resolution; Image segmentation; OBIA; Remote sensing; Image segmentation; High spatial resolution; Geographic object; Remote sensing; OBIA
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2019.02.009

Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.