Segmentation and classification of high spatial resolution images based on Hölder exponents and variance

• Main Authors: Debasish Chakraborty, Sourabh Singh, Dibyendu Dutta
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-01-01
• Series: Geo-spatial Information Science
• Subjects: High-resolution image; texture; segmentation; Hölder exponents; variance; contrast; classification; ISODATA
• Online Access: http://dx.doi.org/10.1080/10095020.2017.1307660
• ISSN: 1009-5020; 1993-5153

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%.