Coastline detection has been of major interest for environmentalists and many methods have been introduced to detect coastline automatically. Remote Sensing techniques are the most promising ones to deliver a satisfactory result in this regard. In our study, the objective was to retrieve performance level of certain image processing techniques vigorously used for the purpose to delineate coastline automatically and they were tested against two images acquired almost on the same period by LISS III and LANDSAT ETM+ sensors. The algorithms used in the study are Water Index, NDVI, Complex Band Ratio, ISODATA, Thresholding, ISH Transfirmation techniques. Accuracy of the shoreline detection by classifying the image in land and water has been tried to be estimated in three ways, firstly with comparison to the visually interpreted high resolution google earth image, secondly field collected GCP data of reference points of classes and thirdly the raw image itself. But problem in temporal disparity caused the constraint doing accuracy assessment from the first two reference data and maps along the coast. As a whole although four techniques among six, show satisfactory results namely density slicing, ISODATA classification, Water Index and ISH transformation technique, in the case of LISS-III and ETM+, Water Index (with kappa value being 0.95 for LISS-III and 0.97 for ETM+) and Intensity-Hue-Saturation transformation techniques give better performance. Sensor to sensor variation might have introduced certain differences in shoreline detection in images of same season with similar tidal influence.
Earth Science Informatics – Springer Journals
Published: Feb 13, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera