Development of Contrast Enhancement Algorithms for Coastal Applications using Satellite Images

Abstract

Remotely sensed satellite images are used in many earth science applications such as geosciences studies, astronomy, and geographical information systems. One of the most important quality factors in satellite images comes from its contrast. Contrast enhancement is frequently referred to as one of the most important issues in image processing. Contrast is created by the difference in luminance reflectance from two adjacent surfaces. Image enhancement is one of the most interesting and important phase in the domain of digital image processing. The main purpose of image enhancement is to bring out details that are hidden in image, or to increase the contrast in a low contrast image. The quality of the remote sensing image depends on the reflected electromagnetic radiation from earth surfaces features. Lack of consistent and similar amounts of energy reflected by different features, results a low contrast satellite image. Enhancement of contrast is desirable for satellite images to identify and extract features, where features are essential in studying earth applications. The present study is carried out with a view to develop contrast enhancement algorithms for coastal applications using satellite images. Histogram Equalization (HE) is an effective and well-known indirect contrast enhancement method, where histogram of the image is modified. Because of stretching the global distribution of the intensity, the information laid on the histogram of the image will be lost by over enhancement and introducing unwanted artefacts. To overcome these drawbacks several HE-based methods are introduced. With the comparative study of existing HE-based methods, the present study has developed contrast enhancement algorithms for coastal applications such as, automatic shoreline detection, suspended sediment transport and land use and land cover assessment for Mangalore Coast, West Coast of India, starting from Thalapady in the South and Mulky in the North.The study has developed an automatic shoreline detection algorithm using clipped histogram equalization and thresholding techniques. Clipped histogram equalization method highlighted the coastal objects and thresholding operation precisely separated the land and water regions. The smoothed shoreline is extracted using Robert’s edge detector. The study area is divided into Mulky-Pavanje rivermouth and NetravatiGurpur rivermouth areas. The shorelines of both the regions are extracted from Indian Remote Sensing Satellite (IRS P6) LISS-III (2005, 2007 and 2010) and IRS R2 LISSIII (2013) satellite images using developed automatic shoreline detection method. The delineated shorelines have been analyzed using Digital Shoreline Analysis System (DSAS), a GIS Software tool for estimation of shoreline change rates through two statistical techniques such as, End Point Rate (EPR) and Linear Regression Rate (LRR). To enhance IRS-P4 OCM Oceasat-2 satellite image for sediment movement direction, study developed Clipped Histogram Equalization and Principal Component Analysis (PCA) based algorithm. The movement of dispersed suspended sediment pattern of Mangalore Coast, West Coast of India is detected and mapped using qualitative analysis. The study is mainly focused on suspended sediment distributions at Netravati-Gurpur Rivermouth along Mangalore Coast. To improve the assessment of land use and land cover, study developed contrast enhancement algorithm using clipped histogram equalization and Principal Component Analysis (PCA). IRS-R2 LISS III 2013 satellite image is used for assess the developed algorithm. For assessment, the study area is divided into MulkyPavanje rivermouth area, New Mangalore Port Trust (NMPT) area and NetravatiGurpur rivermouth area. The IRS-R2 LISS III 2013 satellite image is classified using maximum likelihood supervised classification method by considering GPS values and Google Earth map as reference in selection of training samples during the classification. The developed contrast enhancement algorithm has increased the accuracy assessment of LULC classification to 85.42%, 89.66% and 86.93% for Mulky-Pavanje river mouth area, New Mangalore Port Trust (NMPT) area and Netravati-Gurpur river mouth area respectively.