@article{1434857,
      recid = {1434857},
      author = {Jenicka, S.,},
      title = {Land cover classification of remotely sensed images : a  textural approach /},
      pages = {1 online resource},
      note = {Includes index.},
      abstract = {The book introduces two domains namely Remote Sensing and  Digital Image Processing. It discusses remote sensing,  texture, classifiers, and procedures for performing the  texture-based segmentation and land cover classification.  The first chapter discusses the important terminologies in  remote sensing, basics of land cover classification, types  of remotely sensed images and their characteristics. The  second chapter introduces the texture and a detailed  literature survey citing papers related to texture analysis  and image processing. The third chapter describes basic  texture models for gray level images and multivariate  texture models for color or remotely sensed images with  relevant Matlab source codes. The fourth chapter focuses on  texture-based classification and texture-based  segmentation. The Matlab source codes for performing  supervised texture based segmentation using basic texture  models and minimum distance classifier are listed. The  fifth chapter describes supervised and unsupervised  classifiers. The experimental results obtained using a  basic texture model (Uniform Local Binary Pattern) with the  classifiers described earlier are discussed through the  relevant Matlab source codes. The sixth chapter describes  land cover classification procedure using multivariate  (statistical and spectral) texture models and minimum  distance classifier with Matlab source codes. A few  performance metrics are also explained. The seventh chapter  explains how texture based segmentation and land cover  classification are performed using the hidden Markov model  with relevant Matlab source codes. The eighth chapter gives  an overview of spatial data analysis and other existing  land cover classification methods. The ninth chapter  addresses the research issues and challenges associated  with land cover classification using textural approaches.  This book is useful for undergraduates in Computer Science  and Civil Engineering and postgraduates who plan to do  research or project work in digital image processing. The  book can serve as a guide to those who narrow down their  research to processing remotely sensed images. It addresses  a wide range of texture models and classifiers. The book  not only guides but aids the reader in implementing the  concepts through the Matlab source codes listed. In short,  the book will be a valuable resource for growing  academicians to gain expertise in their area of  specialization and students who aim at gaining in-depth  knowledge through practical implementations. The exercises  given under texture based segmentation (excluding land  cover classification exercises) can serve as lab exercises  for the undergraduate students who learn texture based  image processing.},
      url = {http://library.usi.edu/record/1434857},
      doi = {https://doi.org/10.1007/978-3-030-66595-1},
}