Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

Addink, E.A.; Coillie, Frieke M. B. Van; Jong, S.M. de

doi:10.1016/j.jag.2011.12.001

Cited by 67 publications

(43 citation statements)

References 72 publications

(71 reference statements)

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“…Over the last two decades, advances in earth observation sensors, computer technology and GI science have led to the development of Geographic Object-Based Image Analysis (GEOBIA) as an alternative to the traditional pixel-based image analysis method [32,33]. In [34] they said that "GEOBIA is a systematic framework for geographic object identification, which combines pixels with the same semantic information into an object, thereby generating an integrated geographic object."…”

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

“…GEOBIA is a newly developed area of Geographic Information Science and remote sensing in which the automatic segmentation of images into objects of similar spectral, temporal and spatial characteristics is carried out [35]. In contrast to traditional image analysis, GEOBIA works more like the human eye-brain combination [33]. The latter compares an object's properties such as color, square fit, texture, shape and occurrence with other image objects, along with many other properties, to interpret and analyze what the human eye sees [33,35].…”

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

“…In contrast to traditional image analysis, GEOBIA works more like the human eye-brain combination [33]. The latter compares an object's properties such as color, square fit, texture, shape and occurrence with other image objects, along with many other properties, to interpret and analyze what the human eye sees [33,35].…”

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

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Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Minaei

Kainz

2016

IJGI

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Abstract:The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future.

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Section: Geographic Object Based Image Analysismentioning

confidence: 99%

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

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Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Minaei

Kainz

2016

IJGI

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“…Land-cover mapping is a common task in this context, where it is attempted to generate a partial or full description of a given area from Earth observation imagery, with an emphasis on element geometric and thematic accuracies. Geographic Object-Based Image Analysis (GEOBIA) has emerged as a viable avenue of approaches, or paradigm, to tackle such remote sensing image analysis tasks [1][2][3][4] due to the common spectral-textural-geometric and thematic correlations of elements of interest in satellite imagery [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation algorithms allowing for spatial aggregation in addition to observing spectral characteristics have been shown to be efficient in working towards identifying elements [7,[11][12][13][14]. Based on the characteristics of the desired information products and the nature of the data, the availability of commercial and freeware GEOBIA software [2,[15][16][17] and the extent of the literature [1,2,4], it is shown that GEOBIA is a promising paradigm [1].…”

Section: Introductionmentioning

confidence: 99%

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user OPEN ACCESS Remote Sens. 2014, 6 11853 provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

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Sample Supervised Search‐Centric Approaches in Geographic Object‐Based Image Analysis

Fourie

Schöepfer

2017

Earth Observation for Land and Emergency Monitoring

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Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

Cited by 67 publications

References 72 publications

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Sample Supervised Search‐Centric Approaches in Geographic Object‐Based Image Analysis

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