Classification algorithms

Multispectral pattern recognition

Multispectral remote sensing is the collection and analysis of reflected, emitted, or back-scattered energy from an object or an area of interest in multiple bands of regions of the electromagnetic spectrum (Jensen, 2005). Subcategories of multispectral remote sensing include hyperspectral, in which hundreds of bands are collected and analyzed, and ultraspectral remote sensing where many hundreds of bands are used (Logicon, 1997). The main purpose of multispectral imaging is the potential to classify the image using multispectral classification. This is a much faster method of image analysis than is possible by human interpretation. The Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm used for Multispectral pattern recognition was developed by Geoffrey H. Ball and David J. Hall, working in the Stanford Research Institute in Menlo Park, CA. They published their findings in a technical report entitled: ISODATA, a novel method of data analysis and pattern classification (Stanford Research Institute, 1965). ISODATA is defined in the abstract as: 'a novel method of data analysis and pattern classification, is described in verbal and pictorial terms, in terms of a two-dimensional example, and by giving the mathematical calculations that the method uses. The technique clusters many-variable data around points in the data's original high- dimensional space and by doing so provides a useful description of the data.' (1965, pp v.)ISODATA was developed to facilitate the modelling and tracking of weather patterns. (Wikipedia).

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From playlist Multivariable calculus

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Pattern Matching - Correctness

Learn how to use pattern matching to assist you in your determination of correctness. This video contains two examples, one with feedback and one without. https://teacher.desmos.com/activitybuilder/custom/6066725595e2513dc3958333

From playlist Pattern Matching with Computation Layer

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More Complex Patterns

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From playlist Pattern Matching with Computation Layer

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Characterization of Random, Multivariate Signals

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Multivariable (vector) probability density function representations, including the multivariate Gaussian density. The covariance matrix and in

From playlist Random Signal Characterization

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From playlist Towards urban analytics 2.0

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From playlist Numerical Analysis and Scientific Computing

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From playlist New Directions for Digital Scholarship

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From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics

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From playlist ディープラーニング (Deep Learning — Japanese)

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From playlist Solve Multi-Step Equations......Help!

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From playlist Turing Lectures

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From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics

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From playlist Solve Multi-Step Equations......Help!

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From playlist 画像処理とコンピュータビジョン (Japanese)

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MIT MAS.531 Computational Camera and Photography, Fall 2009 Instructor: Ankit Mohan (guest lecturer) View the complete course: https://ocw.mit.edu/courses/mas-531-computational-camera-and-photography-fall-2009/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61pwA6paIRZ

From playlist MIT MAS.531 Computational Camera and Photography, Fall 2009

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From playlist GSS2012: Deep Learning, Feature Learning

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From playlist Engineering

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Turner: Assembling the Pieces for A Global Biodiversity Monitoring Framework

Woody Turner explains the role of remote sensing in biodiversity monitoring and assessment.

From playlist Spatial Biodiversity Science and Conservation

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Stanford researchers develop drone technology to study secrets of San Francisco Bay

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From playlist Stanford Highlights

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From playlist How to Solve Multi Step Equations with Variables on Both Sides

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Convolutional neural network | Standard deviation | Pixel | Arithmetic mean | K-means clustering