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

By Mohammad D. Hossain, Dongmei Chen. Isprs Journal of Photogrammetry and Remote Sensing, 2019

611 citations recorded by the source corpus.

Abstract 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.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/23c240bf9d7ce46dcf4e96835337e27fcf342cdf

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Citations in the corpus

A Review on UAV-Based Applications for Precision Agriculture (2019, methodology): “A detailed review of algorithms and challenges for OBIA, from a remote sensing perspective, was reported by Hossain and Chen [135].”
A Review on Deep Learning in UAV Remote Sensing (2021, methodology): “Another interesting take on DL-based methods was related to image segmentation in a survey by Hossain et al. [83], which its theme was expanded by Yuan et al. [202] and included state-of-the-art algorithms.”
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, methodology): “Object Proposal Generation goes a step further by suggesting potential object bounding boxes or regions within an image [15, 47].”