2014
DOI: 10.1080/01431161.2014.978042
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Well site extraction from Landsat-5 TM imagery using an object- and pixel-based image analysis method

Abstract: Well sites, including both well pads and exploratory core holes, are small polygonal landscape disturbance features approximately one half to one hectare (0.5-1 ha) in area, resulting from oil and gas exploration activities. Automatic extraction and monitoring of such small features using remote-sensing technology at regional scales has always been desirable for wildlife habitat monitoring and environmental planning and modelling. Due to the vast disturbances of well sites in a province like Alberta, Canada, h… Show more

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Cited by 15 publications
(6 citation statements)
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“…For the extraction of linear features, the method was well-suited to identify dirt roads and infrastructure from PlanetScope and RapidEye imagery, which obtained accurate results and opened up possibilities to apply the methodology to other areas. Consequently, the classification model achieved affordable results with high accuracy by the basis of different satellite data, which is consistent with the findings of other studies [22][23][24]52]. However, as could be seen in the performance of the different classifications, the accuracies of the dirt road and linear infrastructure were strongly related to the spatial resolution of the images through the influence of boundary pixels and influence of finer spatial resolution that increases the spectral-radiometric variation of land cover types [53].…”
Section: Relevance Of the Approachsupporting
confidence: 90%
“…For the extraction of linear features, the method was well-suited to identify dirt roads and infrastructure from PlanetScope and RapidEye imagery, which obtained accurate results and opened up possibilities to apply the methodology to other areas. Consequently, the classification model achieved affordable results with high accuracy by the basis of different satellite data, which is consistent with the findings of other studies [22][23][24]52]. However, as could be seen in the performance of the different classifications, the accuracies of the dirt road and linear infrastructure were strongly related to the spatial resolution of the images through the influence of boundary pixels and influence of finer spatial resolution that increases the spectral-radiometric variation of land cover types [53].…”
Section: Relevance Of the Approachsupporting
confidence: 90%
“…Higher shape values yield image objects with optimal shape homogeneity, while lower shape values produce image objects with optimal radiometric homogeneity. Same as the parameter of shape, the compactness parameter varies between zero and one and controls the degree of object smoothing [39]. These three user-defined parameters are affected by different image spatial resolutions and the sizes of the recognized ground objects [40].…”
Section: Methodsmentioning
confidence: 99%
“…Novack et al [54] showed that RF classifier can evaluate each attribute internally; thus, it is less sensitive to the increase of variables (Tables 5 and 6). The object-based classifier can provide faster and better results and can be easily applied to classify forest types [24,39,40,55]. In addition, this classification method has the ability to handle predictor variables with a multimodal distribution well due to the high variability in time and space [50,56]; especially, no sophisticated parameter tuning is required.…”
Section: Discussionmentioning
confidence: 99%
“…Four key parameters are used to adjust MRS: scale, the weight of color and shape, the weight of compactness and smoothness and the weights of input layers. The scale parameter is used to determine the size of the final image object, which corresponds to the allowed maximum heterogeneity when generating image objects [39,40]. The larger the scale parameter, the larger the size of the generated object, and vice versa.…”
Section: Image Segmentation By Mrsmentioning
confidence: 99%