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Satellite Image Analysis: Clustering and Classification

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Abstract

Satellite images provide much information about the Earth’s surface in a shorter period. The availability of various types of images (multitemporal, multispectral, multiresolution, and multisensory data) became a helpful tool in the evolution of remote sensing-based digital imaging. This chapter explores the applications of classification and clustering techniques when applied on multispectral and hyperspectral satellite images. The automatic analysis of satellite images aids in effective decision-making, thematic map creation, information extraction, disaster management, and field survey to name a few. The applications of satellite image classification in meteorology, oceanography, fishing, agriculture, biodiversity conservation, forestry, intelligence, crisis information, emergency mapping, disaster monitoring, and thermal applications are presented.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, K.S. Institute of Technology, Bengaluru, Karnataka, India

    Surekha Borra

  2. Faculty of Technology and Engineering, Department of ECE, C. U. Shah University, Wadhwan city, Gujarat, India

    Rohit Thanki

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

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Borra, S., Thanki, R., Dey, N. (2019). Applied Examples. In: Satellite Image Analysis: Clustering and Classification. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6424-2_5

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  • DOI: https://doi.org/10.1007/978-981-13-6424-2_5

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