Object recognition and image segmentation: the Feature Analyst® approach

Opitz, David W.; Blundell, S.

doi:10.1007/978-3-540-77058-9_8

Cited by 30 publications

(39 citation statements)

References 5 publications

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“…Feature Analyst (FA) is a GEOBIA application that conducts an internal 'hidden' segmentation of the image that allows to classify and extract only those features belonging to the class of interest. FA uses an inductive machine learning approach for object recognition, exploring both spectral and spatial (contextual) information, and uses several different algorithms depending on the data (Opitz and Blundell, 2008). The classification mode that was used is based on a supervised approach, so the initial step is the identification of training samples for each class, followed by the definition of parameters such as band data type (e.g., reflectance, elevation), and number of bands to use in the classification, the type of input representation pattern and size, and level of aggregation (i.e., minimum object size).…”

Section: Feature Extractionmentioning

confidence: 99%

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Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Freire

Santos

Navarro

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tMany municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000. Ó

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Section: Feature Extractionmentioning

confidence: 99%

“…The results of this first pass can be corrected and added back into the system as knowledge, and the user can adapt the parameters, in an interactive learning process. (Opitz and Blundell, 2008;VLS, 2008). This hierarchical learning adaptive process allows to iteratively improve the image classification.…”

Section: Feature Extractionmentioning

confidence: 99%

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Freire

Santos

Navarro

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Research comparing OBIA software is rare, with most researchers preferring to focus on algorithm development and classification performance of individual software packages [96,97]. Most studies that have focused on comparison have applied the software to high-resolution imagery.…”

Section: Object-based Approachmentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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“…We mapped conifers at a resolution of 1 × 1 m using National Agriculture Imagery Program (NAIP; U.S. Department of Agriculture, 2014) imagery collected in 2010 and 2013 as our reference data and the Feature Analyst ™ toolbox (Overwatch 6 Systems, Ltd., Sterling, Virginia) for Esri ® ArcGIS™ Desktop (Esri, 2013, Release 10.2, Redlands, California). Feature Analyst ™ is an accelerated feature extraction (AFE) method that semi-automates the extraction of target features using a machine learning algorithm trained to delineate image-objects based on the spectral and spatial signatures of defined cell neighborhoods (Opitz and Blundell, 2008). AFE outperforms pixel-based methods (Riggan and Weih, 2009;Weih and Riggan, 2010) and is recognized as one of the most accurate OBIA methods available (Opitz and Blundell, 2008;Tsai and others, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Feature Analyst ™ is an accelerated feature extraction (AFE) method that semi-automates the extraction of target features using a machine learning algorithm trained to delineate image-objects based on the spectral and spatial signatures of defined cell neighborhoods (Opitz and Blundell, 2008). AFE outperforms pixel-based methods (Riggan and Weih, 2009;Weih and Riggan, 2010) and is recognized as one of the most accurate OBIA methods available (Opitz and Blundell, 2008;Tsai and others, 2011). We identified examples of conifer image objects to create 1 × 1 m resolution binary conifer rasters (gridded spatial data that represent conifer presence as cells with values of one) for each sage-grouse population management unit (PMU; Nevada Department of Wildlife, 2014) across the full mapping extent, and conducted extensive analyses of omission and commission to provide estimates of mapping accuracy by PMU.…”

Section: Introductionmentioning

confidence: 99%

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

Coates¹,

Gustafson²,

Roth³

et al. 2017

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit https://www.usgs.gov/ or call 1-888-ASK-USGS (1-888-275-8747).For an overview of USGS information products, including maps, imagery, and publications, visit https:/store.usgs.gov.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Coates, P.S., Gustafson, K.B., Roth, C.L., Chenaille, M.P., Ricca, M.A., Mauch, Kimberly, Sanchez-Chopitea, Erika, Kroger, T.J., Perry, W.M., and Casazza, M.L., 2017, Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales: U.S. Geological Survey Open-File Report 2017-1093, 40 p., https://doi.org/10.3133/ofr20171093. ISSN 2331ISSN -1258 iii PrefaceThis report was developed to provide scientific and corresponding spatially explicit information regarding the distribution and abundance of conifers (namely, singleleaf pinyon [Pinus monophylla], Utah juniper [Juniperus osteosperma], and western juniper [Juniperus occidentalis]) in Nevada and northeastern California. Distributional expansion of conifers into sagebrush ecosystems over the past 150 years is a significant threat to greater sage-grouse (Centrocercus urophasianus; hereinafter, "sagegrouse") populations, as well as those of other sagebrush obligate species. Accordingly, we mapped conifers at a high resolution (1 meter) and derived multiple products (available at https://doi.org/10.5066/F7348HVC) within sage-grouse habitats of Nevada and northeastern California. These products are intended as decision support for land managers, policy-makers, and interested stakeholders to be used for a variety of management and research applications. Users also have the ability to set custom bins representing user-desired ranges of conifer cover for their own applications. Conversion Factors AbstractThe distribution and abundance of pinyon (Pinus monophylla) and juniper (Juniperus osteosperma, J. occidentalis) trees (hereinafter, "pinyon-juniper") in sagebrush (Artemisia spp.) ecosystems of the Great Basin in the Western United States has increased substantially since the late 1800s. Distributional expansion and infill of pinyon-juniper into sagebrush ecosystems threatens the ecological function and economic viability of these ecosystems within the Great Basin, and is now a major contemporary challenge facing land and wildlife managers. Particularly, pinyon-juniper encroachment into intact sagebrush ecosystems has been identified as a primary threat facing populations of greater sage-grouse (Centrocercus urophasianus; hereinafter, "sage-grouse"), which is a sagebrush obligate species. Even seemingly innocuous scatterings o...

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Object recognition and image segmentation: the Feature Analyst® approach

Cited by 30 publications

References 5 publications

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Developments in Landsat Land Cover Classification Methods: A Review

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

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