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Remote SensingTopic 11: Digital Image Analysis

Chapters 6 & 7: Lillesand and KeiferChapters 6 & 15: Avery and Berlin

Image Classification Process1. Define research problem and/or project goals

If research, formulate one or more working hypothesesIf consulting, outline specifications with contractor classification scheme accuracy assessment deliverables

Must consider: time/money facilities/software expertise available

Image Classification Process2. Identify suitable digital data with regard to:

Spatial resolutionSpectral resolutionRadiometric resolutionTemporal resolutionCost & Availability

E.g.Landsat MSS or TM, OLI, SPOT, RADARSATOther commercial high resolution imageryAirborne MSSAerial PhotographyUAV ImageryVideography

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Image Classification Process3. Identify/acquire sources of ground truth for:

training field identificationcluster/information group identificationtest field identification and accuracy assessment

E.g.Field data/notes (ground truth, gps coords)Topographic mapsAerial photographyDigital elevation modelsDLG and TIGER dataLand use/Land cover mapsSoils dataOther mapsHistorical dataInterviews

Image Classification Process4. Data Pre-processing

a. Image Rectification and Restorationobjective is to restore distorted/degraded imagery by:i. correcting geometric distortionii. radiometric calibration/correctioniii. noise removal

i. Geometric correction aligns to established coordinate system compensates for distortion related to data acquisition

• caused by variations in altitude, attitude, velocity, Earth curvature, atm. refraction, relief displacement and across track distortion

Requires resampling of DN values• Nearest Neighbour• Bilinear Interpolation• Cubic Convolution

Image Classification Process4. Data Pre-processing

a. Image Rectification and Restoration

ii. Radiometric Correction techniques used to account for variations in scene brightness: topoographic corrections (sun – object – sensor geometry) BRDF effects of various surfaces sun elevation (when comparing different dates imagery) haze correction DN to % reflectance conversion

• for comparing different sensors

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Image Classification Process4. Data Pre-processing

a. Image Rectification and Restoration

iii. Noise Removal caused by sensor irregularities or extraneous interference can affect entire scan lines or individual pixels includes techniques for correcting:

• systematic striping and banding• dropped scan lines• bit errors

Image Classification Process4. Data Pre-processing

b. Image Enhancementimproves display/facilitates visual interpretation:i. contrast enhancementii. spatial enhancement

i. Contrast Enhancement methods that adjust display values of input DN values

• Grey-Level Thresholding• Level Slicing• Contrast Stretching

– Linear Stretch– Equal Area Stretch– 2% Tails Clipped

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Image Classification Process4. Data Pre-processing

b. Image Enhancement

ii. Spatial Enhancement methods that change DN values based on values of neighbouring

pixels; focal operations• Convolution• Spatial Filters • Edge Enhancement• Fourier Analysis

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Image Classification Process4. Data Pre-processing

c. Transformationsobjective is to derive/extract additional spectral information:i. band ratios

ii. principal components

iii. multi-temporal image integration

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Image Classification ProcessTraining Field Selection and Assessment

a. Visual interpretation/identificationb. Assess spectral response of various featuresc. Develop classification scheme with regard to project/research objectives AND

spectral “separability” of featuresd. Select training (and test) fields (supervised) representative of selected classes

i. Polygon training field selectionii. Seed pixelsiii. Feature space selection from “bi-plots”

e. Evaluate training field and class statisticsi. Ellipse diagrams (from “bi-plots”)ii. Separability analysis (divergence, transformed divergence)iii. Contingency/Error matrix from training field classification

f. Edit classification scheme and training field statisticsg. Return to 5e

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Image Classification Process6. Supervised Classification Algorithms Utilizes training field statistics to assign pixels to a

predetermined set of classesa. Minimum (Euclidean) Distance Calc mean spectral reflectance in each band for each class Distance of each image pixel to class means is calculated Pixel is assigned to class for which minimum distance is smallest In some cases a max. dist. may be provided (not in MultiSpec)

• Pixels exceeding max. dist. are place in “other” category Adv. - is very efficient Disadvantage - doesn’t work so well when variance of some

classes is very high compared to other classes

Training Fields in 2D Feature Space

Minimum (Euclidean) Distance

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Image Classification Process6. Supervised Classification Algorithms

b. Parallelepiped Based on range (min. max.) of DN values for each class Location of each image pixel relative to class range is determined Pixel is assigned to class if it falls within range of DN values If pixels is outside range of DN values for all classes it is placed

in “other” category If ranges overlap and pixel falls in area of overlap

• User can set preference• Or assigned to a special class

Adv: also efficient and eliminates error due to variance Disadvantage: sensitive to covariance within a class

• Common cause of overlapping classes and poor fit of classes ranges

Parallelepiped Classifier

Image Classification Process6. Supervised Classification Algorithms

c. Maximum Likelihood Assumes normal distribution of DN values within each class Class means and covariances between bands are calc. These stats are then used to calc. probability of each image pixel

belonging to each class Pixel is assigned to class for which it has the highest probability

of belonging Min. probability can be set by user; a threshold value

• If a pixel has less than a 5% probability of belonging to any class then it is put in the “other” category

Adv. is that this typically results in higher accuracy than previous two techniques; not sensitive to covariance

Disadvantage is that this is much more computationally intensive

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Maximum Likelihood Classifier

Maximum Likelihood Classifier

Image Classification Process7. Unsupervised Classification Algorithms DOES NOT utilize training field statistics Algorithms place pixels into spectral groups Groups are assigned class names afterwarda. Single Pass User specifies critical distance (in DNs) in line 1 and 2

• E.g. 8 and 16 Value is used to identify DN values of clusters cores All other pixels are assigned to these clusters based on minimum

euclidean distance A minimum cluster size can be specified User has no control over number of clusters produced Only has control over how spectrally similar/different each

cluster will be

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Single Pass – Varying Distance

Image Classification Process7. Unsupervised Classification Algorithms

b. ISODATA Iterative Self-Organizing DATA Analysis User specifies number of clusters, minimum cluster size and

convergence Initialization strategy is used to establish set of clusters Each pixel is assigned based on min. euclidean distance Cluster means are recalculated In second iteration pixels are reassigned to clusters Cluster means are recalculated and the process repeats itself Process ends when user defined convergence value is attained

• Convergence of 98% means process stops when < 2% of pixels switch classes User determines number of classes; difficult to determine

ISODATA – 6 clusters

Compared to Single Pass 6 Clusters?

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Image Classification Process8. Accuracy Assessment Typically done using contingency table, error or

confusion matrix Assesses placement of training (in theory, should be

100%) or test area pixels into classes Identifies errors of:

Omission - pixels that are placed in another class; omitted Comission - pixels placed in a class in which they do not belongAccuracy evaluated in terms of: overall accuracy - % pixels correctly classified producer accuracy – % training/test pixels correct user accuracy - % classified pixels correct Kappa statistic – accounts for number of pixels correctly

classified by chance; always lower than overall accuracy

Contingency Table/ Error Matrix

Hard vs. Fuzzy ClassificationsHard classifications:

Place pixels in one and only one classDoesn’t account for fact that: Landcover typically changes gradually Pixels are often mixtures or “mixels”

Limits accuracy of classification

Fuzzy classifications:Training areas include homogeneous and mixed fieldsMembership grade for each pixel calculatedClassification scheme includes mixed pixels classes

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Integration of Ancillary DataAncillary data include elevation, slope, aspect, soils, geology, hydrology, transportation networks, etc.

Integrations strategies:Geographic Stratification masking/ subsetting imagery before classification

Classifier Operations Ancillary data integrated as a band used during classification

Post-classification Sorting Uses if then & other rules to reclassify/sort data after classification

Object-based ClassificationTraditional spectral classifications

consider only spectral propertiesoperate on a per pixel basis

Object-based classification:also considers spatial prosperities texture, size, shape, elevation, etc.

use process of image segmentation to identify groups of pixels called patches or image objectssegments are then identified and grouped into classes

Ancillary data often used to identify image objects

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