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![Regions of study -North-East Indian cities [15]](profile/Dr-Tasneem-Ahmed/publication/370553178/figure/fig1/AS:11431281156022726@1683310189440/Regions-of-study-North-East-Indian-cities-15.png)
Regions of study -North-East Indian cities [15]
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![Fig. 1. Regions of study -North-East Indian cities [15]](profile/Dr-Tasneem-Ahmed/publication/370553178/figure/fig1/AS:11431281156022726@1683310189440/Regions-of-study-North-East-Indian-cities-15_Q320.jpg)
The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR)...
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... a different type of object-based clustered unsupervised classifier, the processed Sentinel images are considered for implementing the KMeans classification technique. The geographical coordinates for the cities of Ayodhya and Basti are 26.7922° N, 82.1998° E and 26.8140° N, 82.7630° E respectively and the study region on the India map is shown in Fig. 1. ...
Citations
... These vector data applied to Random Forest, KD Tree KNN, and Maximum likelihood classifiers, respectively. ISSN: 1686-6576 (Printed) | ISSN 2673-0014 (Online) | © Geoinformatics International [20] and [21]. The random forest classifier uses the Gini Index as an attribute selection measure, which measures the impurity of an attribute with respect to the classes [22] and [23]. ...
This study presents image classification techniques using Sentinel-1A microwave SAR-C imagery to detect agricultural vulnerability area resulting from a massive flood in Ubon Ratchathani province, Thailand, which occurred in 2019. Two time series of selected images were used in analytical processes: namely S1A_IW_GRDH acquired on August 10th, 2019, representing the pre-flood event, and S1A_IW_GRDH acquired on 9th September 2019 represents the massive flood in this area. Prior to the classification, these data were preformed pre-processing processes, such as calibration, speckle filtering and terrain correction. The preprocessed data were then classified using 3 machine learning classifier algorithms, namely, Random Forest (RF), K-Dimensional Tree (KDTree KNN), and Maximum Likelihood for comparing classification accuracy derived from each classifier. There are 4 land use/land cover (LULC) classes derived from the dataset, i.e., (1) paddy rice, (2) water body, (3) residential area, and (4) vegetation, respectively. The second map was used to determine the extent of flooding and non-water area based on backscattering coefficient derived from Sigma⁰_VV polarization using band math calculation obtained from the histogram. The extracted flooded area aimed at creating the flooded water mask for overlaying with the classified LULC maps derived from each classifier. Finally, the LULC maps were overlaid with flooded event map that occurred on September 9, 2019, for quantifying affected area. The results indicated that paddy rice was damaged by flooded with the area of 98 km² classified by RF achieving the overall accuracy of 94.60%. The KDTree KNN classifier identified the affected area of 85 km² with the overall accuracy of 93%, while the Maximum Likelihood classifier detected the flooded area of 91 km² with the overall accuracy of 93.36%, respectively.
