Extraction of impervious features from spectral indices using artificial neural network

Patel, Nilanchal; Mukherjee, Rohit

doi:10.1007/s12517-014-1492-x

Cited by 36 publications

(17 citation statements)

References 23 publications

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“…Most of the previous studies have used the manual thresholding to generate built-up and non built-up binary images (Garg 2016;Patel & Mukherjee 2015;Xu 2007;Zha et al 2003). An automated thresholding algorithm is swiftly needed to classify the built-up index image into…”

Section: Optimal Thresholding and Otsu's Algorithm For Separating Buimentioning

confidence: 99%

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Bouhennache

Bouden

Taleb-Ahmed

et al. 2018

Geocarto International

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Section: Optimal Thresholding and Otsu's Algorithm For Separating Buimentioning

confidence: 99%

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Bouhennache

Bouden

Taleb-Ahmed

et al. 2018

Geocarto International

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“…When the results were combined, 90-95% overall accuracy was achieved at three study sites. Patel and Mukherjee [28] extracted impervious features by inputting the SAVI, MNDWI, NDBI, BUI, and IBI indices into a backpropagation neural network. However, this approach detected only impervious features; all of the other land cover types were grouped into a single class.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the individual indices, different combinations of indices or modified indices have been developed and used to map land covers [6,[28][29][30]. Although there are various methods for mapping land cover types, the existing approaches face limitations to classify urban land covers.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

Faridatul

2018

IJGI

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Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94-96%, which is superior to the accuracy of the SVM algorithm.

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“…Spectral indices are suitable for the identification of specific features on the ground, such as water, vegetation, or imperviousness, therefore offering more information than raw band data and potentially resulting in higher classification accuracies [30]. Patel et al [31] demonstrated that-when used with spectral indices-ANNs contribute to reaching higher accuracies.…”

Section: Landsat Data Processingmentioning

confidence: 99%

Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)

et al. 2016

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Abstract:The main goal of this paper is to study the effect of the spatio-temporal changes of Land Use/Land Cover (LULC) within the hydrologic regime of the Cervaro basin in Southern Italy. LANDSAT Thematic Mapper (TM) imagery acquisition dates from 1984, 2003, 2009, and 2011 were selected to produce LULC maps covering a time trend of 28 years. Nine synthetic bands were processed as input data identified as the most effective for the Artificial Neural Network (ANN) classification procedure implemented in this case study. To assess the possible hydrological effects of the detected changes during rainfall events, a physically-based lumped approach for infiltration contribution was adopted within each sub-basin. The results showed an increase in flood peak and a decrease of the rangelands, forests, and bare lands between 1984 and 2011, indicating a good correlation between flooding areas and land use changes, even if it can be considered negligible in basins of large dimensions. These results showed that the impact of land use on the hydrological response is closely related to watershed scale.

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Extraction of impervious features from spectral indices using artificial neural network

Cited by 36 publications

References 23 publications

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)

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