Remote sensing image classification (29 Ergebnisse)

Hardcover. Zustand: Very Good +. No Jacket. First Edition. A nice, clean, tight, bright copy. Remainder mark on bottom page edges and a purple "X" on upper right front cover, otherwise would be in near fine condition. Exceptionally clean throughout. Text, photos (double page in color) , diagrams, graphs, display. Everything you need to know. 249 pg. …

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:0126289808.…

Zustand: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains pen markings In poor condition, suitable as a reading copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780849372513.…

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:0126289808.

Zustand: New. In English.

Zustand: New.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Spatial Analysis of Land use Change and Land Degradation | Image Analysis, Classification and change Detection using Remote Sensing and GIS Techniques. | Derege Meshesha | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2009 | VDM Verlag Dr. Müller | EAN 9783639167368 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.…

Paperback. Zustand: Brand New. 4th edition. 532 pages. 9.19x6.13x1.10 inches. In Stock.

Taschenbuch. Zustand: Neu. Metaheuristics for unsupervised classification | Application to remote sensing image segmentation | Lotfi Hocini | Taschenbuch | Englisch | 2021 | Our Knowledge Publishing | EAN 9786203594188 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Paperback. Zustand: Brand New. 220 pages. 9.19x6.13x0.50 inches. In Stock.

Taschenbuch. Zustand: Neu. Dr. Pei-Gee Ho dissertation | MULTIVARIATE TIME SERIES MODEL BASED SUPPORT VECTOR MACHINE FOR MULTICLASS REMOTE SENSING IMAGE CLASSIFICATION AND REGION SEGMENTATION | Pei-Gee Ho | Taschenbuch | 124 S. | Englisch | 2009 | LAP LAMBERT Academic Publishing | EAN 9783838303529 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

hardcover. Zustand: Good.

Taschenbuch. Zustand: Neu. Remote Sensing in Rio de Janeiro City | Automatic Land-Cover Classification Derived from High-Resolution IKONOS Satellite Image in the Urban Atlantic Forest | Luiz F. Guanaes | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639057133 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Zustand: New.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: - exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; - discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; - describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; - explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; - combines explanation of the algorithms with case studies and practical applications.…

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world¿s daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time.…

Einband - fest (Hardcover). Zustand: New.

Zustand: New. In English.

Zustand: New.

Zustand: New. In English.

Zustand: New. Morton John Canty, now semi-retired, was a senior research scientist in the Institute for Bio- and Geosciences at the Juelich Research Center in Germany. He received his PhD in Nuclear Physics in 1969 at the University of Manitoba, Canada and, after post-doc.

Hardcover. Zustand: Brand New. 189 pages. 9.25x6.25x0.75 inches. In Stock.

Zustand: New.

Hardcover. Zustand: Brand New. 192 pages. 9.25x6.00x0.65 inches. In Stock.

Buch. Zustand: Neu. Neuware - The fifth edition of this core textbook in advanced remote sensing continues to maintain its emphasis on statistically motivated, data-driven techniques for remote sensing image analysis. The theoretical substance remains essentially the same, with new material on convolutional neural networks, transfer learning, image segmentation, random forests, and an extended implementation of sequential change detection with radar satellites. The tools which apply the algorithms to real remote sensing data are brought thoroughly up to date. As these software tools have evolved substantially with time, the fifth edition replaces the now obsolete Python 2 with Python 3 and takes advantage of the high-level packages that are based on it, such as Colab, TensorFlow/KERAS, Scikit-Learn, and the Google Earth Engine Python API.New in the Fifth Edition: - Thoroughly revised to include the updates needed in all chapters because of the necessary changes to the software. - Replaces Python 2 with Python 3 tools and updates all associated subroutines, Jupyter not Elektronisches Buch and Python scripts. - Presents easy, platform-independent software installation methods with Docker containers. - Each chapter concludes with exercises complementing or extending the material in the text. - Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). - Examines deep learning examples including TensorFlow and a sound introduction to neural networks. This new text is essential for all upper-level undergraduate and graduate students pursuing degrees in Geography, Geology, Geophysics, Environmental Sciences and Engineering, Urban Planning, and the many subdisciplines that include advanced courses in remote sensing. It is also a great resource for researchers and scientists interested in learning techniques and technologies for collecting, analyzing, managing, processing, and visualizing geospatial datasets.…

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…