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Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
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Añadir al carritoTaschenbuch. Condición: Neu. Advanced Image Processing Techniques for Remote Sensing Applications | A Systematic Approach to Estimate Crop Yield using NOAA/Landsat 8 Satellites Data Through Denoising and Enhancement | Kanagala Sateesh Kumar (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206781790 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Sep 2023, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main objective of this book is to propose a systematic approach to estimate crop yield (Paddy, Groundnut) for remote sensing applications using NOAA and Landsat 8 satellite images. In general the received imagery are noisy in nature. So, to tackle this issue spatial and frequency domain based denoising techniques were proposed and discussed in detail by implemented on both NOAA and Landsat 8 images. After this, In this book, empirical crop yield estimation models are proposed for both NOAA and Landsat 8 satellite data to estimate crop yields of both Paddy, groundnut with remote sensing images and meteorological parameters. By this, it can be said that this research work can be used as a secondary opinion in the estimation of crop yield over the study area. 168 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: Majestic Books, Hounslow, Reino Unido
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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: moluna, Greven, Alemania
EUR 61,15
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar Kanagala SateeshK Sateesh Kumar, working as an Assistant Professor in SNIST, Hyderabad. His interested areas are Digital Image Processing, Remote Sensing and Machine Learning etc. Prof. G. Sreenivasulu, working as Professor in .
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 101,30
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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Sep 2023, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 79,90
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main objective of this book is to propose a systematic approach to estimate crop yield (Paddy, Groundnut) for remote sensing applications using NOAA and Landsat 8 satellite images. In general the received imagery are noisy in nature. So, to tackle this issue spatial and frequency domain based denoising techniques were proposed and discussed in detail by implemented on both NOAA and Landsat 8 images. After this, In this book, empirical crop yield estimation models are proposed for both NOAA and Landsat 8 satellite data to estimate crop yields of both Paddy, groundnut with remote sensing images and meteorological parameters. By this, it can be said that this research work can be used as a secondary opinion in the estimation of crop yield over the study area.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206781798 ISBN 13: 9786206781790
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 80,86
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main objective of this book is to propose a systematic approach to estimate crop yield (Paddy, Groundnut) for remote sensing applications using NOAA and Landsat 8 satellite images. In general the received imagery are noisy in nature. So, to tackle this issue spatial and frequency domain based denoising techniques were proposed and discussed in detail by implemented on both NOAA and Landsat 8 images. After this, In this book, empirical crop yield estimation models are proposed for both NOAA and Landsat 8 satellite data to estimate crop yields of both Paddy, groundnut with remote sensing images and meteorological parameters. By this, it can be said that this research work can be used as a secondary opinion in the estimation of crop yield over the study area.