Загрузка страницы

Remote Sensing Image Analysis and Interpretation: Image analysis and interpretation basics

Second lecture in the course 'Remote Sensing Image Analysis and Interpretation' covering the basics of image analysis and interpretation, change detection and classification.

slides: https://uni-bonn.sciebo.de/s/U6K56GFUaYW9FGt

Lecturer: Ribana Roscher
Winter term 2020/2021, University of Bonn

Видео Remote Sensing Image Analysis and Interpretation: Image analysis and interpretation basics канала Ribana Roscher
Показать
Комментарии отсутствуют
Введите заголовок:

Введите адрес ссылки:

Введите адрес видео с YouTube:

Зарегистрируйтесь или войдите с
Информация о видео
5 ноября 2020 г. 3:10:59
01:02:14
Другие видео канала
Advanced Machine Learning for Remote Sensing: Train neural networksAdvanced Machine Learning for Remote Sensing: Train neural networksAdvanced Machine Learning for Remote Sensing: WelcomeAdvanced Machine Learning for Remote Sensing: WelcomeAdvanced Machine Learning for Remote Sensing: Neural NetworksAdvanced Machine Learning for Remote Sensing: Neural NetworksExplain it to me - Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable MLExplain it to me - Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable MLAdvanced Machine Learning for Remote Sensing: BasicsAdvanced Machine Learning for Remote Sensing: BasicsWhat Identifies a Whale by its Fluke? On the Benefit of Interpretable ML for Whale IdentificationWhat Identifies a Whale by its Fluke? On the Benefit of Interpretable ML for Whale IdentificationExplainable machine learning (2022, 5th lecture): Interpret. by backward prop. + concept discoveryExplainable machine learning (2022, 5th lecture): Interpret. by backward prop. + concept discoveryExplainable machine learning (2022, 4th lecture): Local model-agnostic methodsExplainable machine learning (2022, 4th lecture): Local model-agnostic methodsLearning with Real-World and Artificial Data for Improved Vehicle Detection in Aerial ImageryLearning with Real-World and Artificial Data for Improved Vehicle Detection in Aerial ImageryExplainable machine learning (2022, 2nd lecture): Looking into a neural networkExplainable machine learning (2022, 2nd lecture): Looking into a neural networkExplainable machine learning (2022, 1st lecture): IntroductionExplainable machine learning (2022, 1st lecture): IntroductionSemCity Toulouse: A Benchmark for Building Instance Segmentation in Satellite ImagesSemCity Toulouse: A Benchmark for Building Instance Segmentation in Satellite ImagesMulti-Modal Deep Learning with Sentinel-3 Observations for the Detection of Oceanic Internal WavesMulti-Modal Deep Learning with Sentinel-3 Observations for the Detection of Oceanic Internal WavesAdvanced Machine Learning for Remote Sensing: Representation learningAdvanced Machine Learning for Remote Sensing: Representation learningRemote Sensing Image Analysis and Interpretation: Density estimationRemote Sensing Image Analysis and Interpretation: Density estimationExplainable machine learning (2022, 3rd lecture): Global model-agnostic methodsExplainable machine learning (2022, 3rd lecture): Global model-agnostic methodsRemote Sensing Image Analysis and Interpretation: Introduction to Remote SensingRemote Sensing Image Analysis and Interpretation: Introduction to Remote SensingRemote Sensing Image Analysis and Interpretation: Feature extraction and image segmentationRemote Sensing Image Analysis and Interpretation: Feature extraction and image segmentationAdvanced Machine Learning for Remote Sensing: Convolutional and Recurrent Neural NetworksAdvanced Machine Learning for Remote Sensing: Convolutional and Recurrent Neural NetworksRemote Sensing Image Analysis and Interpretation: Classification with Bayes' theoremRemote Sensing Image Analysis and Interpretation: Classification with Bayes' theorem
Яндекс.Метрика