Comparison of Geo-Object Based and Pixel-Based Change Detection of Riparian Environments using High Spatial Resolution Multi-Spectral Imagery

Johansen, Kasper; Arroyo, Lara A.; Phinn, Stuart; Witte, Christian

doi:10.14358/pers.76.2.123

Cited by 94 publications

(39 citation statements)

References 33 publications

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“…For VHR imagery such a strategy is considered to generally obtain better results than a pixel-based classification strategy (Blaschke, 2010;Dragut et al, 2010;Johansen et al, 2010). Objects provide valuable information regarding texture, shape, context and spectral characteristics (for example mean and standard deviation of the NDWI).…”

Section: Advantages and Disadvantages Of Vhr Satellite Imagery In Vecmentioning

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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Section: Advantages and Disadvantages Of Vhr Satellite Imagery In Vecmentioning

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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“…Compared to pixel-based approaches, GEOBIA facilitates multiscale analysis allowing to outline the landscape at different levels, reducing small spurious changes and provides a significant increase in the number of features that can be examined in a given analysis (HayandCastilla, 2008). Previous studies have shown that improved results and higher accuracy in image classification is achieved when GEOBIA is applied (Desclée et al, 2006;Im et al, 2008b;Johansen et al, 2010;Lingcao et al, 2010;McDermid et al, 2008;Robertson and King, 2011;Zhou et al, 2008a). The image is divided into sub-objects where scale and merging may be adjusted according to a proposed objective.…”

Section: Introductionmentioning

confidence: 99%

Land cover and land use characterization with GEOBIA in the Pitangui River Basin area, Parana-Brazil

Prichoa¹,

Ribeiro²,

Holgado³

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:GEOBIA -Geographic Object-Based Image Analysis -is considered to be a technique that takes advantage of several Remote Sensing dimensions -like spectral, spacial, morphologic and contextual information -together with functionalities of Geographic Information Systems (GIS) as well. GEOBIA unit of analysis is an image object that obtains complete information. It includes texture, shape and spatial relations to neighboring objects and ancillary spatial data to different resolutions. In this study, it was sought to identify -in the spatial, spectral and texture descriptor group generated with GEOBIA -those that most represent the study unity. The technique was applied in part of the scene 221/77 of Landsat 5 TM satellite from year 2010 in color composition R5G4B3. This area belongs to the Pitangui river basin located at the center-east portion of the State of Paraná -Brazil, between coordinates 600.987m E and 7.240.620m N to 614.973m E and 7.231.568m N and zone 22S. Segmentation tests were done adjusting scale and merging levels according to the proposed objective (analysis of land use). Using a group of 35 descriptors with spatial, spectral and texture characteristics, Principal Component Analysis (PCA) and clustering were respectively applied with the aim of decreasing dimensionality of descriptors and grouping similar ones according to the shortest euclidean distance. From 14 resulting descriptors analyzed in the dendrogram generated in the clustering procedure, only compactness and solidity were grouped in the shortest euclidean distance. In the descriptors graph, maximum and minimum values for compactness and solidity have presented linear increase enabling the previous analysis.

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“…Typical cases include image classification (Gao et al, 2011;Laliberte et al, 2012;Salehi et al, 2012;Du et al, 2013;Rasi et al, 2013), thematic information extraction or object recognition (Walter 2004;Huang and Zhang, 2008;Hu and Weng, 2011;Sebari and He, 2013;Benarchid and Raissouni, 2013;d'Oleire-Oltmanns et al, 2014), and change detection (Johansen et al, 2010;Lu et al, 2011;Hebel et al, 2013). Man-made object classification or recognition is common when HSR images are used.…”

Section: Introductionmentioning

confidence: 99%

Sliver Removal in Object-Based Change Detection from VHR Satellite Images

Barazzetti

2016

Photogram Engng Rem Sens

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Comparison of Geo-Object Based and Pixel-Based Change Detection of Riparian Environments using High Spatial Resolution Multi-Spectral Imagery

Cited by 94 publications

References 33 publications

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

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

Land cover and land use characterization with GEOBIA in the Pitangui River Basin area, Parana-Brazil

Sliver Removal in Object-Based Change Detection from VHR Satellite Images

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