Version 1
: Received: 20 August 2019 / Approved: 21 August 2019 / Online: 21 August 2019 (10:30:16 CEST)
How to cite:
Neware, R.; Thakare, M. Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images. Preprints2019, 2019080225. https://doi.org/10.20944/preprints201908.0225.v1
Neware, R.; Thakare, M. Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images. Preprints 2019, 2019080225. https://doi.org/10.20944/preprints201908.0225.v1
Neware, R.; Thakare, M. Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images. Preprints2019, 2019080225. https://doi.org/10.20944/preprints201908.0225.v1
APA Style
Neware, R., & Thakare, M. (2019). Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images. Preprints. https://doi.org/10.20944/preprints201908.0225.v1
Chicago/Turabian Style
Neware, R. and Mansi Thakare. 2019 "Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images" Preprints. https://doi.org/10.20944/preprints201908.0225.v1
Abstract
The technique of obtaining information or data about any feature or object from afar, called in technical parlance as remote sensing, has proven extremely useful in diverse fields. In the ecological sphere, especially, remote sensing has enabled collection of data or information about large swaths of areas or landscapes. Even then, in remote sensing the task of identifying and monitoring of different water reservoirs has proved a tough one. This is mainly because getting correct appraisals about the spread and boundaries of the area under study and the contours of any water surfaces lodged therein becomes a factor of utmost importance. Identification of water reservoirs is rendered even tougher because of presence of cloud in satellite images, which becomes the largest source of error in identification of water surfaces. To overcome this glitch, the method of the shape matching approach for analysis of cloudy images in reference to cloud-free images of water surfaces with the help of vector data processing, is recommended. It includes the database of water bodies in vector format, which is a complex polygon structure. This analysis highlights three steps: First, the creation of vector database for the analysis; second, simplification of multi-scale vector polygon features; and third, the matching of reference and target water bodies database within defined distance tolerance. This feature matching approach provides matching of one to many and many to many features. It also gives the corrected images that are free of clouds.
Keywords
water bodies; satellite images; vector data; SVM; positive and negative buffering; polygons
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
