Satellite Image Analysis: Clustering and Classification

Borra, Surekha; Thanki, Rohit M.; Dey, Nilanjan

doi:10.1007/978-981-13-6424-2

Cited by 50 publications

(38 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Band ratioing is one of the existing change detection and land-use mapping techniques (Borra, Thanki, & Dey, 2019;Lu, Mausel, Brondizio, & Moran, 2004a). Band ratioing can reduce the effect of illumination variation resulting from environmental factors such as topography (Borra et al, 2019;Mather & Koch, 2011). Al-Saud (2005), in a study of Al-Hasa in Saudi Arabia, applied a set of band ratios to discriminate land features in an arid environment.…”

Section: Introductionmentioning

confidence: 99%

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Aina

Adam

Ahmed

et al. 2019

European Journal of Remote Sensing

View full text Add to dashboard Cite

This paper examines the application of remote sensing, based on the Vegetation-Impervious surface-Soil (V-IS) model and spatial metrics, in an urban analysis for promoting sustainability and understanding urban growth theory. In order to improve the accuracy of land-cover classification, spectral angle mapping (SAM), spectral mixture analysis (SMA) and band ratioing were applied on satellite images for land-cover classification and comparison of the discrimination efficiency of these techniques. For the SMA, subsets of the Landsat (2, 3, 4, 5, and 7) and ASTER (1, 2, and 3N) images were selected. After endmember extraction and purification, the Bayesian probability of each component was computed and used for spectral unmixing. The classified images of different years were compared to analyze the changes in land-use and spatial pattern using V-IS , a form of percentage of landscape (PLAND) and annualized urban sprawl index (AUSI). The result indicates that the performance of band ratioing (69% accuracy) is not asgood as that of SAM (75%) and SMA (86%) in discriminating between vegetationand agricultural land. It is concluded from the land-use analysis that the growth dynamics substantiate the urban theory of diffusion and coalescence and urban growth management strategies have not been completely successful.

show abstract

Section: Introductionmentioning

confidence: 99%

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Aina

Adam

Ahmed

et al. 2019

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…SAVI values usually range from −1.5 to 1.5, with lower values depicting bare ground and higher values indicating higher ground coverage by vegetation (Lam and Remmel 2010). 5) We classified the SAVI-transformed clusters in six classes (including five classes for vegetation habitat types and one class for water/snow) using an unsupervised maximum likelihood algorithm (Borra et al 2019). The chosen number of classes reflected a visual evaluation of dendrograms produced during the classification process, which showed the distance between each pair of sequentially merged classes.…”

Section: Appendixmentioning

confidence: 99%

Survey of the vascular plants of Alert (Ellesmere Island, Canada), a polar desert at the northern tip of the Americas

Desjardins¹,

Lai²,

Payette³

et al. 2021

CheckList

View full text Add to dashboard Cite

Long-term monitoring is critical to guide conservation strategies and assess the impacts of climatic changes and anthropogenic activities. In High Arctic ecosystems, information on distribution and population trends of plants is dramatically lacking. During two field expeditions in 2018 and 2019, we conducted a systematic floristic survey together with opportunistic collecting in the polar desert surrounding Alert (Nunavut, Canada) to update past vascular plant inventories. We recorded 58 species, of which 54 species were recorded over the last seven decades, and four species that are additions to the local flora (Draba pauciflora R. Brown, Festuca edlundiae S.G. Aiken, Consaul, & Lefkovitch, Festuca hyperborea Holmen ex Frederiksen, and ×Pucciphippsia vacillans (T. Fries) Tzvelev). With the addition of 19 species that were previously reported but not found in our survey, we estimate the species richness in the study area at 77 species.

show abstract

“…Haze pollution monitoring is another application of satellite remote sensing [22] which results in high quality measurements. Various machine learning techniques including texture analysis [23], clustering and classification [24], as well as visualization, analysis and interpretation [25] are among the approaches which have already been applied to the remote sensing dataset. The analysis of water resources is crucial because of its social effect on the life of people living in the nearby area.…”

Section: ) To Have Control Over Width Of the Prediction Intervalmentioning

confidence: 99%

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Khanesar

Branson

2021

IEEE Sensors J.

View full text Add to dashboard Cite

This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.

show abstract

Satellite Image Analysis: Clustering and Classification

Cited by 50 publications

References 0 publications

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Survey of the vascular plants of Alert (Ellesmere Island, Canada), a polar desert at the northern tip of the Americas

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Contact Info

Product

Resources

About