Abstract
This paper presents the segmentation technique used to segment the Worldview-2 high resolution satellite multispectral (MS) images. First the spectral features like Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Modified Soil Adjusted Vegetation Index (MSAVI) are considered to extract the spectral features from the MS image. Next the MS image is segmented by using the over segmented k-means algorithm with novel initialization (OSKNI) method. The proposed method performs well in terms of User’s accuracy (UA), Producer’s accuracy (PA) and overall segmentation accuracy (OVA) compared to the existing k-means algorithm.
Highlights
Image segmentation is the underlying process in majority of the applications of image processing like remote sensing, computer vision etc
The spectral features considered in this paper are Simple Ratio (SR) (Birth and McVey, 1968), Normalized Difference Vegetation Index (NDVI) (Rouse et al, 1973), Soil Adjusted Vegetation Index (SAVI) (Huete, 1998) and Modified Soil Adjusted Vegetation Index (MSAVI) (Qi et al, 1994)
We considered Worldview-2 images of Gulbarga district, 50 subset images of size 256 x256 are considered to test the proposed algorithm out of which three images results are presented in this paper
Summary
Image segmentation is the underlying process in majority of the applications of image processing like remote sensing, computer vision etc. Image segmentation is the process fall under region-based techniques of classifying remote sensing images before the classification of segments takes place (Banerjee et al, 2014; Paclıka et al, 2003). With respect to the remote sensing, segmentation is the way towards the outlining singular areas of homogeneous earth cover, while segmentation is the ensuring procedure of recognizing the depicted region as kinship to a particular earth cover (Johnson and Xie, 2011). This paper proposes an unsupervised clustering method of segmenting the RS images into the sectors of uniform areas using spectral features (Kumar et al, 2018). The spectral features considered in this paper are SR (Birth and McVey, 1968), NDVI (Rouse et al, 1973), SAVI (Huete, 1998) and MSAVI (Qi et al, 1994)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Remote Sensing and GIS
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
