Pre-processing of high resolution satellite images for sea bottom classification

Deidda, Monica

doi:10.5721/itjrs20124417

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…Their applications included LULC mapping [29], [30], forest volume estimation [31], land surface temperature [32], [33], and coastal dynamics [34]. Nevertheless, few of them have concerned the relationship between the LULC classification accuracy and pansharpening or atmospheric correction.…”

Section: Introductionmentioning

confidence: 99%

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Lin

Tsogt

et al. 2015

Information Processing in Agriculture

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Changes of Land Use and Land Cover (LULC) affect atmospheric, climatic, and biological spheres of the earth. Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction. This paper examined effects of pansharpening and atmospheric correction on LULC classification. Object-Based Support Vector Machine (OB-SVM) and Pixel-Based Maximum LikelihoodClassifier (PB-MLC) were applied for LULC classification. Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image. Nevertheless, pansharpening plays much more important roles on the classification accuracy than the atmospheric correction. It can help to increase classification accuracy by 12% on average compared to the ones without pansharpening. PB-MLC and OB-SVM achieved similar classification rate. This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82% and 89% respectively. A combination of atmospheric correction, pansharpening, and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

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Section: Introductionmentioning

confidence: 99%

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Lin

Tsogt

et al. 2015

Information Processing in Agriculture

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“…The histogram matching was performed to normalize the radiometric differences between the two satellite images. Digital image classification usually employs the spectral information of a geographical entity represented by the digital numbers derived from a single or combination of spectral bands and attempts to classify each individual pixel based on this information, which is termed as spectral pattern recognition (Deidda & Sanna, 2012; Kuang et al, 2001).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

Thakur

Dutta²,

Bijalwan

et al. 2022

Land Degrad Dev

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The present study attempts to understand land use land cover change dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur District in Madhya Pradesh, India through geospatial techniques. Land use and land cover (LULC) change detection analysis was performed digitally classifying LANDSAT-5 (2001) as well as LANDSAT-8 (2020) satellite data using a maximum likelihood algorithm. Results revealed that area under dense native vegetation decreased drastically (by 13.74 km 2 ) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 km 2 ). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico-chemical parameters. Studies suggested that soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty-year time-scale indicates that the reclamation activities are still in its infancy.A land degradation vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60% of the area is highly vulnerable to degradation. The land degradation index (LDI) inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001-2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.coal mining, decadal changes, heat plot, land degradation vulnerability index, land use land cover | INTRODUCTIONThe Anthropocene is a time of great risk for humanity due to emerging as well as rapidly evolving environmental consequences (Crutzen, 2002;Kareiva, 2014;Keys et al., 2019). Global climate change, degradation of natural resources, biodiversity loss, deterioration of ecosystem services pose huge challenge for all species on

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“…The WV-2 images were successively processed through radiometric correction and atmospheric correction steps [46], and the image DN values were converted to radiance and then to top-of-atmosphere reflectance concerning the sensor specifications released by DigitalGlobe, to remove image noise generated during transmission [45]. The image was also geometrically corrected according to the GPS coordinates of landmark features collected in the field at Qinglan Harbor, georeferenced to the World Geodetic System (WGS84) 1984 datum and the Universal Transverse Mercator (UTM) zone 49 N projection, with an error control of 2 pixels.…”

Section: Worldview-2 Imagesmentioning

confidence: 99%

High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data

et al. 2021

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Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the vegetation’s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation.

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Pre-processing of high resolution satellite images for sea bottom classification

Cited by 12 publications

References 4 publications

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data

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