Coastline Extraction Using Support Vector Machine from Remote Sensing Image

Abstract

In recent years, support vector machine (SVM) has been widely applied in remote sensing image classification, since its experience can also minimize errors and maximize the geometric characteristics of the edge area. In this article SVM classification algorithm will be introduced the remote sensing extraction coastline. Fujian Province Landsat7 ETM + image will be a test region to be classified the image and extract the shoreline. Then based on the coastline formula calculate modified the shoreline in the ArcGIS and completed the extraction of coastline

Shoreline Detection using Optical Remote Sensing: A Review

“…In the case of unsupervised classification, one can use a Principal Component Analysis (PCA) to determine the class number. For coastline detection, PCA can be a powerful tool that allows researchers to obtain uncorrelated pixels with high variance for a better classification [124].…”

“…The watershed transform allows the definition of the regions from the gradients of the image. The region growing approach is also used in [124].…”

“…The region growing methods are fast and conceptually simple, but they are very sensitive to the distribution of the objects in the image. [124,129] Watershed transform…”

“…Its drawback is that it does not retain the radiometry of the input multispectral image [133]. [133] Principal Component Analysis The PCA allows the use of smaller databases and reduction of noise [120,124] Object-oriented classification It reduces salt-and-pepper effects commonly noted in pixel-based remote sensing image classification. [120,128,134] Texture analysis-based methods Texture analysis groups together a set of techniques allowing quantifying the different grey-levels present in an image in terms of intensity and distribution in order to calculate a number of parameters characteristic of the texture to be studied.…”

Multitemporal image analysis for monitoring fuzzy shorelines

“…Meanwhile, another group of image classification to generate shoreline position and its changes is supervised classification which requires relevant training areas based on a priori knowledge of the users of study area characteristics. Several studies on this method include support vector machines (SVM) (Hannv et al, 2013;Kalkana et al, 2013;Yin and He, 2011) and maximum likelihood classification (MLC) (Duru, 2017;Sekovski et al, 2014;Tamassoki et al, 2014). Since supervised classification takes advantage of information provided from training samples, the classification works effectively.…”