Performance evaluation of urban areas Land Use classification from Hyperspectral data by using Mahalanobis classifier

Nagne, Ajay D.; Dhumal, Rajesh K.; Vibhute, Amol D.; Rajendra, Yogesh D.; Gaikwad, Sandeep V.; Kale, K. V.; Mehrotra, S. C.

doi:10.1109/isco.2017.7856023

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The atmospheric correction of these multispectral data was conducted using the ENVI FLAASH model. The bad bands and bad columns of the Hyperion image were removed because many of their bands show low signal-to-noise ratio or other problems [ 59 , 71 ]. Atmospheric correction was then also implemented using the FLAASH algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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Section: Methodsmentioning

confidence: 99%

“…Hyperion is a Hyperspectral instrument on the Earth Observation 1 (EO-1) spacecraft launched on 21 November 2000 [ 59 ], with 7.5 km coverage [ 60 ]. It has a total of 242 bands from 357 to 2577 nm with a spectral resolution of 10 nm and a spatial resolution of 30 m [ 61 , 62 ].…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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“…Variance and covariance are figured in so that clusters that are highly varied lead to similarly varied classes, and vice versa. For example, when classifying urban areas typically a class whose pixels vary widely correctly classified pixels may be farther from the mean than those of a class for water, which is usually not a highly varied class [36].…”

Section: Mahalanobis Distancementioning

confidence: 99%

Untitled

2018

IJET

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“…A geographic information system (GIS) is a valuable geospatial tool commonly used to assess land cover characteristics (Bhuiyan et al, 2020; Jahan et al, 2021). Secondary sources of Google map data have been used for land cover scenario analysis (Nagne et al, 2018). The objective of the article is to measure the variability of land price before and after the rail infrastructure development project through the OLS model particularly in the control and catchment areas in the study region.…”

Section: Introductionmentioning

confidence: 99%

Land Price Variability Assessment of a Transport Infrastructure Project in Pabna District, Bangladesh

Rana,

Rasel,

Hasan

et al. 2024

Environment and Urbanization ASIA

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Transport infrastructure development projects play a vital role in improving accessibility, which has dynamic effects on the regional economy as well as increases property values. This research aims to assess the land price variability in control and catchment areas before and after the Dasuria-to-Dhalarchar railway development project in Pabna district, Bangladesh. A set of independent variables was considered to measure the correlation between development project and land price through a questionnaire survey and focus group discussion. The impact of a railway project on land price was determined by the ordinary least squares method, after which the method was justified through Cook’s distance, P-P plot and scatter plot. The data analysis reveals a strong association between land price and distance to a central business district. The data shows that the land price increased significantly after implementation of the railway project with different intensities, as functional characteristics vary among selected stations.

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Performance evaluation of urban areas Land Use classification from Hyperspectral data by using Mahalanobis classifier

Cited by 9 publications

References 16 publications

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

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Land Price Variability Assessment of a Transport Infrastructure Project in Pabna District, Bangladesh

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