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 Multispectral image classification i.e. separate in feature space i.e. separate in feature space Supervised classification Supervised classification Supervised classification: MDM Supervised classification: parallelepiped (‘box’) Supervised classification: parallelepiped (‘box’) Supervised classification: Gaussian maximum likelihood Supervised classification: Gaussian maximum likelihood Classification Accuracy Postclassification filtering Postclassification filtering
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Environmental Remote Sensing GEOG 2021

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 Сл Текст Сл Текст 1 Environmental Remote Sensing GEOG 17 maximum likelihood. Now we use probability 2021. Lecture 4 Image classification. rather than distance in feature space 2 Purpose. categorising data data Which class is each pixel “most likely” to abstraction / simplification data belong to?? 17. interpretation mapping for land cover 18 Supervised classification: Gaussian mapping use land cover class as a maximum likelihood. Now pixel 1 correctly surrogate for other information of assigned to corn class Much more interest (ie assign relevant sophisticated BUT is computationally information/characteristics to a land expensive compared to distance methods. cover class). 2. 18. 3 Multispectral image classification. 19 Supervised classification: decision Very widely used method of extracting tree. Classify in steps, where the thematic information Use multispectral classifier has only to be able to (and other) information Separate different distinguish between two or more classes at land cover classes based on spectral each step can combine various types of response, texture, …. i.e. separability in classifiers as appropriate using such “feature space”. 3. methods. 19. 4 Basis for 'classifying'. method: 20 Classification Accuracy. How do we pattern recognition use any/all of the tell if classification is any good? following properties in an image to Classification error matrix (aka confusion differentiate between land cover classes:- matrix or contingency table) Need “truth” spectral spatial temporal directional data – sample pixels of known classes How [time / distance-resolved (LIDAR)]. 4. many pixels of KNOWN class X are 5 Spatial pattern recognition use incorrectly classified as anything other spatial context to distinguish between than X (errors of omission)? So-called different classes e.g. measures of image Type 2 error, or false negative Divide texture, spatial context of 'objects ' correctly classified pixels in each class derived from data. Temporal pattern of truth data by COLUMN totals (Producer’s recognition the ability to distinguish Accuracy) How many pixels are incorrectly based on spectral or spatial classified as class X when they should be considerations may vary over the year use some other known class (errors of variations in image DN (or derived data) commission)? So-called Type 1 error, or over time to distinguish between different false positive Divide correctly classified cover types e.g. variations in VI over pixels in each class by ROW totals (User’s agricultural crops. 5. Accuracy). 20. 6 Directional pattern recognition 21 Classification Accuracy. 21. surface with different structures will 22 Can use original training data to test tend to give different trends in BUT…. …this only tells us how well the reflectance as a function of view and classifier can classify the training areas illumination angles Spectral pattern Ideally, use an independent set of samples recognition most widely used distinguish to give a better 'overall' accuracy between different land cover classes from estimate. 22. differences in the spectral reflectance 23 Unsupervised Classification (or more typically, image DN) in different (clustering). Little input from user wavebands. 6. required (few assumptions) BUT means 7 i.e. separate in feature space. Use results hard to interpret (may not different spectral response of different represent classes we recognise) cluster materials to separate e.g. plot red v NIR pixels in feature space based on some DN values…. 7. measure of their proximity interpretation 8 Approaches to Classification. We need of results / assigned classes can be some form of automated (rule-based) useful, e.g. in picking up variations classification algorithm to allow us to within what would otherwise be distinguish one surface type from another distinguished as a single class e.g. Supervised Classification Unsupervised stressed/unstressed crop in a single Classification. 8. field) clusters can be of little intrinsic 9 Supervised classification. training value in themselves e.g. sunlit trees, stage (significant user input/expertise) shaded trees is perhaps not a useful Identify areas of cover types of interest discrimination if one simply wants to (map, ground survey, spectral classify 'trees', and so clusters may have characteristics) in bands of an image. to be combined. 23. From Lillesand, Kiefer and Chipman (2004). 24 Unsupervised Classification: K-means. 9. A large number of clustering algorithms 10 Supervised classification: training exist K-means input number of clusters stage. areas of interest delineated by desired algorithm typically initiated with user spectral information on the cover arbitrarily-located 'seeds' for cluster types is gathered for these areas Training means each pixel then assigned to closest data (subset of whole) These are “classes” cluster mean revised mean vectors are then we will place all remaining pixels in computed for each cluster repeat until according to their DN values Can plot in some convergence criterion is met (e.g. feature space – do we see clusters? 10. cluster means don't move between 11 Supervised classification: iterations) computationally-expensive classification stage. Need rule(s) to because it is iterative. 24. decide into which class we put given pixel 25 Unsupervised classification: ISODATA e.g. Minimum distance to means (MDM) for (Iterative self-organising data analysis) each land cover class, calculate the mean algorithm. Same as K-means but now we can vector in feature space (i.e. the mean vary number of clusters (by splitting / value in each waveband) Put every pixel merging) Start with (user-defined number) into nearest class/cluster define a limit randomly located clusters Assign each beyond which a pixel remains unclassified pixel to nearest cluster (mean spectral a simple and fast technique but has major distance) Re-calculate cluster means and limitations… 11. standard deviations If distance between 12 Supervised classification. Feature two clusters < some threshold, merge space clusters E.g. 2 channels of them If standard deviation in any one information Are all clusters separate? 12. dimension > some threshold, split into 13 Supervised classification: MDM. Find two clusters Delete clusters with small closest cluster mean for each pixel Simple number of pixels Re-assign pixels, and quick BUT what about points 1, 2? i.e. re-calculate cluster statistics etc. until MDM insensitive to variance of clusters changes of clusters < some fixed Can we improve? 13. threshold. 25. 14 Supervised classification: 26 ISODATA example: 2 classes, 2 bands. parallelepiped (‘box’). Assign boundaries DN Ch 2. 26. around the spread of a class in feature 27 Hybrid Approaches. useful if large space i.e. take account of variance variability in the DN of individual typically use minimum/maximum of DN in a classes use clustering concepts from particular class to define limits, giving unsupervised classification to derive a rectangle in 2D, box in 3D (if we have sub-classes for individual classes, > 2 bands) etc. pixels outside of these followed by standard supervised methods. regions are unclassified (which is good or can apply e.g. K-means algorithm to (test) bad, depending on what you want!!) subareas, to derive class statistics and problems if class regions overlap or if use the derived clusters to classify the high covariance between different bands whole scene requirement that all classes (rectangular box shape inappropriate) can of interest are represented in these test modify algorithm by using stepped areas clustering algorithms may not always boundaries with a series of rectangles to determine all relevant classes in an image partially overcome such problems simple e.g. linear features (roads etc.) may not and fast technique takes some account of be picked-up by the textural methods variations in the variance of each class. described above. 27. 14. 28 Postclassification filtering. The 15 Supervised classification: result of a classification from RS data parallelepiped (‘box’). Simple boxes can often appear rather 'noisy' Can we defined by min/max limits of each training aggregate information in some way? class. But overlaps……..? …so use stepped Simplest & most common way is majority boxes. 15. filtering a kernel is passed over the 16 Supervised classification: Gaussian classification result and the class which maximum likelihood. assumes data in a occurs most commonly in the kernel is used class are (unimodal) Gaussian (normal) May not always be appropriate; the distributed class then defined through a particular method for spatial aggregation mean vector and covariance matrix of categorical data of this sort depends calculate the probability of a pixel on the particular application to which the belonging to any class using probability data are to be put e.g. successive density functions defined from this aggregations will typically lose scattered information we can represent this as data of a certain class, but keep equiprobability contours & assign a tightly-clustered data. 28. pixel to the class for which it has the 29 Postclassification filtering. Majority highest probability of belonging to. 16. filter. 29. 17 Supervised classification: Gaussian Environmental Remote Sensing GEOG 2021.ppt
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Environmental Remote Sensing GEOG 2021

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