'Supervised classification' diaporamas de présentation

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1.22k views • 103 slides

Image Classification

Image Classification. Chapter 12. Intro. Digital image classification is assigning pixels to classes (categories) Each pixel has as many digital values as there are bands Compare the values to pixels of known composition and assign them accordingly Each class (in theory) is homogenous.

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Data-Intensive Computing with MapReduce

Data-Intensive Computing with MapReduce. Session 7: Clustering and Classification. Jimmy Lin University of Maryland Thursday, March 7, 2013.

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Supervised classification

Statistical Analysis of Microarray Data. Supervised classification. Application: ALL versus AML (data from Golub et al., 1999). Data source: Golub et al (1999). First historical publication searching for molecular signatures of cancer type. Training set 38 samples from 2 types of leukemia

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Data Mining: A Closer Look

Data Mining: A Closer Look. Chapter 2. 2.1 Data Mining Strategies (p35). Moh!. Classification. Learning is supervised. The dependent variable is categorical. Well-defined classes. Current rather than future behavior. Estimation. Learning is supervised.

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Anti-Learning

Anti-Learning. Adam Kowalczyk Statistical Machine Learning NICTA, Canberra (Adam.Kowalczyk@nicta.com.au). National ICT Australia Limited is funded and supported by:. 1. Anti-learning Elevated XOR Natural data Predicting Chemo-Radio-Therapy (CRT) response for Oesophageal Cancer

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Integration of sensors for photogrammetry and remote sensing

Integration of sensors for photogrammetry and remote sensing. 8 th semester, MS 2005. Overview on satellites and sensors operating in the optical spectrum. Earth observing system (EOS) Landsat S POT NOAA Other satellite programs Exercise: supervised classification of a Landsat TM image.

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Remote Sensing - I

Remote Sensing - I. - Optical Range - Image Classification. Image Optimization versus Classification. Landsat-TM of Morocco/Anti-Atlas. Classification Product. Enhanced (optimized) Image Data. Object Space. Feature Space. Result Theme Space. Feature- extraction. Classifier.

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An Overview of RS Image Clustering and Classification

An Overview of RS Image Clustering and Classification. by Miles Logsdon with thanks to Robin Weeks Frank Westerlund. What is Remote Sensing and Image Classification?.

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Places @ Facebook

Places @ Facebook. Justin Moore 6/12/2014. Our goal is to enable engagement based on a world-class POI database. We believe that competitive quality in the following 5 dimensions is a sufficient basis for competitive product experiences:

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Image Classification

Image Classification. Digital Image Processing Techniques. Image Restoration Image Enhancement Image Classification. Image Classification: the art and science of using the computer to interpret the image. Why do it?.

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Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

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Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

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Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021. Lecture 5 Image classification. Purpose. categorising data data abstraction / simplification data interpretation mapping for land cover mapping

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Section 1.1

Section 1.1 . Background. Objectives. Discuss some of the history of data mining. Define data mining and its uses. Defining Characteristics. 1. The Data Massive, operational, and opportunistic 2. The Users and Sponsors Business decision support 3. The Methodology

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Frank Falzone Ross Meyer FR 3262 10.December.2012

Identifying Potential Plantation Sites for Hybrid Hazelnut Production in a Micro-Cooperative Context. Frank Falzone Ross Meyer FR 3262 10.December.2012. Outline. Hybrid hazels in context Desired project output Data acquisition Procedures Accuracy assessment Results

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Exercise #5: Supervised Classification

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An Image C lassification of Khartoum, Sudan

by Lia Sullivan. An Image C lassification of Khartoum, Sudan. Landsat ETM+ 2006 Image of Khartoum. Global Land Cover Facility. www.landcover.org. Delineate the urban extent of Khartoum. Create 5 output classes in the process: urban desert fallow agriculture water

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Classification of Snow Leopard habitat in Mt. Everest National Park

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W. Dean Hively 1 , Ali Sadeghi* 1 , Megan Lang 1 , Varaprasad Bandaru 2 , and Greg McCarty 1

Natural Resources Conservation Service. Deriving Satellite Crop Rotation Maps for Distributed Modeling of Water Quality in the Choptank River watershed. W. Dean Hively 1 , Ali Sadeghi* 1 , Megan Lang 1 , Varaprasad Bandaru 2 , and Greg McCarty 1 * (301) 504-6693; Ali.Sadeghi@ars.usda.gov

213 views • 1 slides