Unsupervised image segmentation evaluation and refinement using a multi-scale approach

Johnson, Brian Alan; Xie, Zhixiao

doi:10.1016/j.isprsjprs.2011.02.006

Cited by 295 publications

(219 citation statements)

References 26 publications

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“…The variation in object size was particularly evident in the Landsat mosaic, which comprised a number of large water bodies, for which both OTB and RSGISLib produced a single object, whereas eCognition split them into a number of smaller objects It should be noted that in these tests, only the default/recommended parameters were used. In Shepherd et al [24], it was shown that by optimizing the parameters of both RSGISLib and eCognition to maximize the segmentation quality metric of Johnson and Xie [26], similar results were obtained from both algorithms. Therefore, it is important that the parameters of any segmentation algorithm are chosen to give the best results based on the available imagery and application.…”

Section: Segmentation Comparisonmentioning

confidence: 87%

“…The scale, or size, of the segmentation is controlled by k, where smaller values of k produce larger objects. A comparison of the algorithm to the multi-resolution segmentation algorithm within eCognition [25] was made by Shepherd et al [24], and the results were found to be of comparable quality when the "optimal" parameters were selected using the Johnson and Xie [26] method of accessing segmentation quality.…”

Section: Segmentationmentioning

confidence: 99%

“…As input to the segmentation algorithm, a stacked HH composite (1996,2007,2010), that had been Lee-filtered and calibrated to dB, was used (Figure 5a). The parameters chosen for the segmentation were experimentally derived (using the V and M I metrics [24,26]); K-means was seeded with 50 cluster centres, and the elimination was run until all objects were at least 50 pixels in size. A subset of the segmentation if shown in Figure 5b.…”

Section: Segmentationmentioning

confidence: 99%

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A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

et al. 2014

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A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate Remote Sens. 2014, 6 6112 the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia.The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.

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Section: Segmentation Comparisonmentioning

confidence: 87%

Section: Segmentationmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

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A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

et al. 2014

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“…The algorithm presents two key parameters: the number of clusters k, and the minimum object size. These parameters have been optimised using the Johnson and Xie method [16] for measuring segmentation quality.…”

Section: Object-based Image Analysismentioning

confidence: 99%

Price Volatility and Water Demand in Agriculture: A Case Study of the Guadalquivir River Basin (Spain)

Pérez-Blanco¹,

Gómez²,

Berbel³

et al. 2014

Economics of Water Management in Agriculture

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“…Supervised classification is predicated upon user knowledge of the realities of a given study site; clusters of training pixels in a satellite image that are representative of any number of user-defined land-cover categories of interest are first identified by the user, then used to train a specified classification algorithm to locate and identify similar pixels in the remainder of the image [35]. While the a priori input of information has been identified as the main disadvantage of the supervised approach due to its potentially difficult, subjective, and time-consuming nature [36], the site-specific scope of our study as well as our use of high-resolution Google Earth™ imagery to promote the accurate identification of training pixels (see Section 2.3 for more detailed methodology) mitigated these concerns. Other common classification methods such as unsupervised classification and object based image analysis (OBIA) were considered less optimal for this study when compared with the supervised approach.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Unsupervised image segmentation evaluation and refinement using a multi-scale approach

Cited by 295 publications

References 26 publications

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

Price Volatility and Water Demand in Agriculture: A Case Study of the Guadalquivir River Basin (Spain)

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

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