8 Journal of Global Resources Volume 6 (01) August 2019-January 2020 Page 08-15 UGC–CARE Listed Journal in Group D ISSN: 2395-3160 (Print), 2455-2445 (Online) 02 INCORPORATING SPECTRAL INDICES AND TEXTURAL FEATURES FOR IMPROVED CLASSIFICATION ACCURACY TOWARDS SEMI-AUTOMATED RIVER SAND MAPPING USING HIGH-RESOLUTION MULTISPECTRAL SATELLITE IMAGERY Virat Arora 1,2 , S. S. Rao 1 , E. Amminedu 2 and P. Jagadeeswara Rao 2 1 National Remote Sensing Centre, Hyderabad, India 2 Dept. of Geo-Engineering, Andhra University College of Engineering, Visakhapatnam, India Email: aroravirat@gmail.com Abstract: For the ephemeral river channels in semi-arid regions of India, after every Monsoon season, prompt preparation of river sand distribution maps is often necessary for river sand auditing before resuming the sand mining operations. The process can be readily assisted by classifying the satellite-based remotely sensed imagery, although often confronted by limited accuracy levels arising due to poor distinguishing capability among the spectrally similar class- categories. This study aims to improve the classification accuracy targeting river sand deposits by systematically examining the effects of including spectral indices and textural features in the feature-space while classification. Two most common classification algorithms, viz. Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) classification were used. The results show that SVM performed even better when Normalized Difference Vegetation Index (NDVI) and correlation texture feature computed at 3x3 window size were included in the feature-space comprising original spectral bands. Key words: River Sand, MLC, SVM, Spectral Indices, Textural Features Introduction River sand is a valuable natural resource with its major utilization in the construction industry. With the increasing pressure of rapid infrastructural development, there has been an equivalent rise in the market demand of river sand. Consequently, the limited sources of river sand are placed with an undue strain in terms of disturbed riverine environment. To ensure the sustainable use of river sand, regular audits of their reserves are often recommended by the policy makers. Preparation of river sand distribution maps is an important step in river sand audits. Regular updating of such maps requires frequent cumbersome field surveying spells that usually encompasses involvement of multiple stakeholders. In this scenario, often, old data are relied upon and are carried forward year-by-year without updating due to lack of availability of resources, and more importantly, due to lack of knowledge of appropriate technique (Mitra and Singh, 2015). Remote sensing data has demonstrated potential in the applications involving generation of planimetric maps depicting river sand deposits. For example, Ramkumar et al. (2015) made an attempt to identify the active-channel sand bars within Kaveri River through visual interpretation from IRS 1B LISS-III imagery of the year 2008, compared it with Survey of India toposheet of the year 1971, and found the areal extent of these sand bars to be increasing at a rate of 1.05 km 2 per year (Ramkumar et al., 2015). For eliminating the problem of biasedness in image interpretation, and towards introducing automation, numerous classification algorithms have been developed by the research community such as pixel-based, object-based and knowledge-based classification algorithms that use spectral, spatial or temporal information, or any combination of these, via unsupervised clustering methods or supervised learning methods (Lu and Weng, 2007). For example, Leckie et al. (2005) performed pixel-based image classification on 0.8 m spatial resolution aerial imagery dataset with eight spectral bands for mapping of stream features such as deep, moderate and shallow classes of water along with sand, gravel, cobble, and rocky areas. Feature-space optimization is a critical step towards increasing the classification accuracy. Features such as spectral signatures, vegetation indices, textural and terrain features are some of the possible variables in any classification process (Lu and Weng, 2007). The key is to improve the class-separability by incorporating the distinguishing characteristics among the land cover classes present in the study area. Various spectral indices, when included in the