Journal ArticleDOI
Assessing object-based classification: advantages and limitations
Desheng Liu,Fan Xia +1 more
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TLDR
The results based on a QuickBird satellite image indicate that segmentation accuracies decrease with increasing segmentation scales and the negative impacts of under-segmentation errors become significantly large at large scales.Abstract:
The advantages of object-based classification over the traditional pixel-based approach are well documented. However, the potential limitations of object-based classification remain less explored. In this letter, we assess the advantages and limitations of an object-based approach to remote sensing image classification relative to a pixel-based approach. We first quantified the negative impacts of under-segmentation errors on the potential accuracy of object-based classification by developing a new segmentation accuracy measure. Then we evaluated the advantages and limitations of object-based classification by quantifying their overall effects relative to pixel-based classification, with respect to their classification units and features at multiple segmentation scales. The results based on a QuickBird satellite image indicate that (1) segmentation accuracies decrease with increasing segmentation scales and the negative impacts of under-segmentation errors become significantly large at large scales and (2...read more
Citations
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Change detection from remotely sensed images: From pixel-based to object-based approaches
TL;DR: This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context, followed by a review of object-basedchange detection techniques.
Journal ArticleDOI
Unsupervised image segmentation evaluation and refinement using a multi-scale approach
Brian Alan Johnson,Zhixiao Xie +1 more
TL;DR: Comparison of single- and multi-scale segmentations shows that identifying and refining under- and over-segmented regions using local statistics can improve global segmentation results.
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Developments in Landsat Land Cover Classification Methods: A Review
Darius Phiri,Justin Morgenroth +1 more
TL;DR: It is suggested that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science, and many advancements in specific classifiers and algorithms have occurred in the last decade.
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Satellite remote sensing of grasslands: from observation to management
TL;DR: In this article, the authors reviewed the current status of grassland monitoring/observation methods and applications based on satellite remote sensing data, and identified the key remaining challenges and some new upcoming trends for future development.
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Object-Based Image Analysis in Wetland Research: A Review
TL;DR: This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives, and summarizes the progress and scope of O BIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIB capacity in the future wetland studies.
References
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Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more
TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.
Journal ArticleDOI
Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information
TL;DR: In this article, an object-oriented image analysis software, eCognition, is proposed to integrate remote sensing imagery and GIS for mapping, environmental monitoring, disaster management and civil and military intelligence.
Multiresolution Segmentation-an optimization approach for high quality multi-scale image segmentation
TL;DR: In this paper, a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques is proposed for object oriented image processing, which aims for an universal high-quality solution applicable and adaptable to many problems and data types.
Journal ArticleDOI
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery
TL;DR: In this paper, the authors evaluated the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data.
Book ChapterDOI
Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline
TL;DR: A formal definition of GEOBIA is provided, a SWOT1 analysis of its potential is conducted, and its main tenets and plausible future are discussed.