Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico

Aguirre‐Gutiérrez, Jesús; Seijmonsbergen, A.C.; Duivenvoorden, Joost F.

doi:10.1016/j.apgeog.2011.10.010

Cited by 148 publications

(75 citation statements)

References 47 publications

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“…With the increasing applications of the object-based approach on widely available high-spatial resolution imagery, therefore, it would be interesting to test the object-based updating/backdating approach for change analysis using high-spatial resolution image data. The BOB approach had similar accuracies for land cover classification and change analysis with those from previous studies (Aguirre -Gutiérrez, Seijmonsbergen, & Duivenvoorden, 2012;Chen, Chen, Shi, & Yamaguchi, 2012;Desclee, Bogaert, & Defourny, 2006). But we did not conduct any manual editing for refinement, and did not use any ancillary data to aid in the change detection and classification.…”

Section: Discussionsupporting

confidence: 62%

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

153

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Section: Discussionsupporting

confidence: 62%

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

153

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“…The research result that object-based classifier give the best overall accuracy in 86.2%. Similar research are also done by (Bruce, 2008), (Oruc et al, 2004), (Xiaoxia et al, 2005), (Qian et al, 2007), (Gholoobi et al, 2010), (Weih and Riggan, 2010), (Avci et al, 2011), (Aguirre-Gutiérrez et al, 2012), which compared pixelbased and object-based method. The result showed that objectbased better than pixel-based method with varying percentage of accuracy.…”

Section: Introductionsupporting

confidence: 62%

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach For the Pattern Consistency of Multi Scale Landcover

juniati

Arrofiqoh

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

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“…These features are extremely relevant for classification purposes and are lacking within traditional pixel-based classification methods (Hay and Castilla 2008;Blaschke, 2010). Assigning a larger weight to particular layers increases the influence of these layers on the segmentation boundaries (Aguirre-Gutierrez et al, 2012). In consequence, the bands and indices that typically show a good distinction between water bodies and other landscape elements (NDWI, NDVI, NIR1, NIR2, PAN) were given a larger weight in the segmentation process (a weight of 4 instead of 1 for all other layers).…”

Section: Object-based Classificationmentioning

confidence: 99%

“…The compactness factor controls for the smoothness of the objects borders (eCognition, 2013). The ESP Tool selected the most suitable scale parameter based on the PAN band (Dragut et al, 2010), while the shape and compactness parameters were determined by a trial-and-error approach and visual interpretation of the results (similar to the procedure followed by Im et al (2008), Halabisky et al (2011) and Aguirre-Gutierrez et al (2012)). The optimal scale (14), shape (0.5) and compactness (0.5) parameters were chosen in such a way that no undersegmentation occurred.…”

Section: Object-based Classificationmentioning

confidence: 99%

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

et al. 2014

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Abstract. The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m 2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

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Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico

Cited by 148 publications

References 47 publications

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach For the Pattern Consistency of Multi Scale Landcover

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

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