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Titlebook: Land Cover Classification of Remotely Sensed Images; A Textural Approach S. Jenicka Book 2021 The Editor(s) (if applicable) and The Author(

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發(fā)表于 2025-3-21 20:06:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Land Cover Classification of Remotely Sensed Images
副標(biāo)題A Textural Approach
編輯S. Jenicka
視頻videohttp://file.papertrans.cn/581/580545/580545.mp4
概述The book helps the reader in implementing the concepts through the Matlab source codes listed.The book is immensely useful for Computer Science and Civil Engineering undergraduates as well post-gradua
圖書封面Titlebook: Land Cover Classification of Remotely Sensed Images; A Textural Approach S. Jenicka Book 2021 The Editor(s) (if applicable) and The Author(
描述.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 sou
出版日期Book 2021
關(guān)鍵詞Image Processing; Texture Analysis; Remotely Sensed Images; Texture Models; Segmentation; remote sensing/
版次1
doihttps://doi.org/10.1007/978-3-030-66595-1
isbn_softcover978-3-030-66597-5
isbn_ebook978-3-030-66595-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:23:52 | 只看該作者
S. JenickaThe book helps the reader in implementing the concepts through the Matlab source codes listed.The book is immensely useful for Computer Science and Civil Engineering undergraduates as well post-gradua
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發(fā)表于 2025-3-22 02:25:21 | 只看該作者
http://image.papertrans.cn/l/image/580545.jpg
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發(fā)表于 2025-3-22 12:35:07 | 只看該作者
https://doi.org/10.1007/978-3-030-66595-1Image Processing; Texture Analysis; Remotely Sensed Images; Texture Models; Segmentation; remote sensing/
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發(fā)表于 2025-3-22 14:14:41 | 只看該作者
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發(fā)表于 2025-3-22 17:27:20 | 只看該作者
Supervised Texture-Based Segmentation Using Basic Texture Models,ntation of a gray scale image using a basic texture model (like gray level co-occurrence matrix (GLCM), uniform local binary pattern (ULBP), wavelet and Gabor wavelet-based texture representation) and k-NN classifier. A listing of the relevant Matlab source codes is given in the chapter ..
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發(fā)表于 2025-3-23 00:11:24 | 只看該作者
Overview of Spatial Data Analysis and Other Land Cover Classification Methods,cussed. Furthermore this chapter describes generic remotely sensed image analysis approaches and land cover classification methods like per pixel-based, per field-based, sub-pixel-based, and super pixel-based methods.
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ithout an official document that provides clear directions to all institutional levels, there are initiatives that impact on best practices within the University and end up reflecting positively in society. From this work, other institutions can benefit from the initiatives and have the awareness th
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