A comparison of hyperspectral data and worldview-2 images to detect impervious surface

Taherzadeh, Ebrahim; Shafri, Helmi Zulhaidi Mohd; Mansor, Shattri; Ashurov, Ravshan

doi:10.1109/whispers.2012.6874305

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…angular second moment, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, and difference entropy; these parameters were calculated 20 times for (d, 0), (0, d), (d, d), and (d, -d), where distance d can take the value of 1 to 5; 3) Twenty run length matrix-based characteristics [12,14]: run length nonuniformity, grey-level nonuniformity, long-run emphasis, short-run emphasis, and fraction of image in runs; these parameters were computed four times (for horizontal, vertical, 45°, and 135° directions); 4) Five absolute gradient-based parameters [12]: mean, variance, skewness, kurtosis, and percentage of pixels with nonzero gradient; 5) Five first-order autoregressive model characteristics [11,15,16]…”

Section: Methodsmentioning

confidence: 99%

Study of Selected Textural Features Properties on Asbestos Roof Images

Książek¹

2018

geom

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This research concentrated on an assessment of the sensitivity of five selected textural feature groups focused on changes in contrast, brightness, sharpness, and rotation of image. The analyzed texture images were real samples of asbestos roofs. During the study, MaZda software was used for determining 259 different textural features. The obtained results were analyzed to indicate insensitive parameters while transforming the sample image. There were no textural features found in the received results, which were simultaneously resistant to all of the implemented changes .

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Section: Methodsmentioning

confidence: 99%

Study of Selected Textural Features Properties on Asbestos Roof Images

Książek¹

2018

geom

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“…Construction land, also known as the impervious surface, is an important part of urban land features and also an indicator of urban development. It also provides the scientific reference significance for the future planning and development of the city (Dougherty, Dymond, Goetz, Jantz, & Goulet, 2004;Linden & Hostert, 2009;Taherzadeh, Shafri, Mansor, & Ashurov, 2012). In this article, the three landscape pattern indexes of architectural land are selected to study the development of the city, that is, PD, LPI and ENN_MN.…”

Section: Analysis Of Landscape Patterns At the Level Of Land Use Typesmentioning

confidence: 99%

Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images

Lin

Guo

Yan

et al. 2018

European Journal of Remote Sensing

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Weihai's urban development model is representative of coastal cities in China. Landsat MSS/ TM/OLI images were used to extract the land use types of Weihai from 1985-2015 using the object-oriented support vector machine (SVM) classification method. The landscape pattern indexes were calculated based on the classification results of land use. The temporal and spatial characteristics of land use and landscape pattern were analyzed by considering Weihai's economic development. The overall kappa (OK) coefficients were greater than 83% and the overall accuracy (OA) values were greater than 85%. Over 80% of both producer's accuracy (PA) and user's accuracy (UA) values of different land use types were greater than 85%. Changes in land use and landscape patterns were closely related to the local economic development. The areas of cultivated land and woodland continued to decrease. The expansion of construction land came mainly from the cultivated land and woodland as with as very little from waters. Under the combined effect of economic development and human activities, the landscape pattern of Weihai as a whole has been decentralized. The internal structure is relatively complex and the fragmentation of land use is increasing. The development model of Weihai is the expansion-aggregation-expansion.

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“…There are different approaches that can be adopted for this reconnaissance. The use of remotely sensed data has proven to be a good instrument for identifying and evaluating the status of roofing made of asbestos fiber cement materials, e.g., by using images from space-borne remote sensing [4][5][6][7][8]. Due to the spectral characteristics of asbestos roofs, hyperspectral remotely sensed images are often used for asbestos roofing identification [3,[9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A QGIS Tool for Automatically Identifying Asbestos Roofing

Tommasini

Bacciottini

Gherardelli

2019

IJGI

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Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on the geographic information system open source software, QGIS) that is conceived for automatically identifying buildings with asbestos roofing. The area under investigation is the metropolitan area around Prato (Italy). The performance analysis of this system was carried out by classifying images that were acquired by the WorldView-3 sensor. These images are available at a low cost when compared with those obtained by means of aerial surveys, and they provide adequate resolution levels for roofing classification. The tool, a QGIS plugin, has shown fairly good performance in identifying asbestos roofing, with some false negatives and some false positives when applying a per-pixel classification. A performance improvement is obtainable when considering the percentage of asbestos pixels that are contained in each roof of the analyzed image. This value is also available with the plugin. In the future, this tool should make it possible to monitor the asbestos roof removal process over time in the area of interest, in accordance with other image data that give evidence of such removals.

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A comparison of hyperspectral data and worldview-2 images to detect impervious surface

Cited by 6 publications

References 9 publications

Study of Selected Textural Features Properties on Asbestos Roof Images

Study of Selected Textural Features Properties on Asbestos Roof Images

Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images

A QGIS Tool for Automatically Identifying Asbestos Roofing

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