1 Some elements of photo Shape Size Pattern Color (tone, hue) Texture Shadows Site Association interpretation Olson, C. E., Jr Elements of photographic interpretation common to several sensors. Photogrammetric Engineering, Vol. 26(4):
2 Tone Tone refers to the relative brightness or color of objects in an image. Generally, tone is the fundamental element for distinguishing between different targets or features. Variations in tone also allows the elements of shape, texture, and pattern of objects to be distinguished.
3 Shape Shape refers to the general form, structure, or outline of individual objects. Shape can be a very distinctive clue for interpretation. Straight edge shapes typically represent urban or agricultural (field) targets, while natural features, such as forest edges, are generally more irregular in shape, except where man has created a road or clear cuts. Farm or crop land irrigated by rotating sprinkler systems would appear as circular shapes.
4 Size Size of objects in an image is a function of scale. It is important to assess the size of a target relative to other objects in a scene, as well as the absolute size, to aid in the interpretation of that target. A quick approximation of target size can direct interpretation to an appropriate result more quickly. For example, if an interpreter had to distinguish zones of land use, and had identified an area with a number of buildings in it, large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use.
5 Pattern Pattern refers to the spatial arrangement of visibly discernible objects. Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern. Orchards with evenly spaced trees, and urban streets with regularly spaced houses are good examples of pattern.
6 Texture Texture refers to the arrangement and frequency of tonal variation in particular areas of an image. Rough textures would consist of a mottled tone where the grey levels change abruptly in a small area, whereas smooth textures would have very little tonal variation. Smooth textures are most often the result of uniform, even surfaces, such as fields, asphalt, or grasslands. A target with a rough surface and irregular structure, such as a forest canopy, results in a rough textured appearance. Texture is one of the most important elements for distinguishing features in radar imagery.
7 Shadow Shadow is also helpful in interpretation as it may provide an idea of the profile and relative height of a target or targets which may make identification easier. However, shadows can also reduce or eliminate interpretation in their area of influence, since targets within shadows are much less (or not at all) discernible from their surroundings. Shadow is also useful for enhancing or identifying topography and landforms, particularly in radar imagery.
8 Association Association takes into account the relationship between other recognizable objects or features in proximity to the target of interest. The identification of features that one would expect to associate with other features may provide information to facilitate identification. In the example given above, commercial properties may be associated with proximity to major transportation routes, whereas residential areas would be associated with schools, playgrounds, and sports fields. In our example, a lake is associated with boats, a marina, and adjacent recreational land.
10 Digital Image Analysis Digital vs. analog Digital number (DN), brightness value (BV) Pixels Bands (channels) Resolution Platform and sensors GIS
11 Measuring EMR Sources of Noise Scattering Absorption Refraction T = transmission of atmosphere
12 EMR Color Spectrum
13 Cathode-Ray Tube (CRT) & RGB Color
14 Why do plants appear green?
15 TM/ETM+ bands B G R
17 Pre-processing Preprocessing functions involve those operations that are normally required prior to the main data analysis and extraction of information, and are generally grouped as radiometric or geometric corrections. Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor. Geometric corrections include correcting for geometric distortions due to sensor-earth geometry variations, and conversion of the data to real world coordinates (e.g. latitude and longitude) on the Earth's surface.
18 Pre-processing operations, sometimes referred to as image restoration and rectification, are intended to correct for sensor- and platform-specific radiometric and geometric distortions of data. Stripping Dropped lines Radiometric corrections may be necessary due to variations in scene illumination and viewing geometry, atmospheric conditions, and sensor noise and response. Each of these will vary depending on the specific sensor and platform used to acquire the data and the conditions during data acquisition. Also, it may be desirable to convert and/or calibrate the data to known (absolute) radiation or reflectance units to facilitate comparison between data.
19 Geometric registration and correction via resampling Geometric registration Bilinear interpolation Nearest neighbor resampling Cubic convolation
20 Image enhancement The simplest type of enhancement is a linear contrast stretch. This involves identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transformation to stretch this range to fill the full range. A linear stretch uniformly expands this small range to cover the full range of values from 0 to 255. This enhances the contrast in the image with light toned areas appearing lighter and dark areas appearing darker, making visual interpretation much easier. This graphic illustrates the increase in contrast in an image before (top) and after (bottom) a linear contrast stretch.
21 Spatial filtering Low pass filter Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. Average and median filters, often used for radar imagery (and described in Chapter 3), are examples of low-pass filters. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. One implementation of a high-pass filter first applies a low-pass filter to an image and then subtracts the result from the original, leaving behind only the high spatial frequency information.
22 Directional or edge detection filter Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. These filters are useful in applications such as geology, for the detection of linear geologic structures.
23 Image Transformation Image division or spectral ratioing is one of the most common transforms applied to image data. Image ratioing serves to highlight subtle variations in the spectral responses of various surface covers. By ratioing the data from two different spectral bands, the resultant image enhances variations in the slopes of the spectral reflectance curves between the two different spectral ranges that may otherwise be masked by the pixel brightness variations in each of the bands. One widely used image transform is the Normalized Difference Vegetation Index (NDVI) which has been used to monitor vegetation conditions on continental and global scales using the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the NOAA series of satellites
25 1 Coastal water mapping, soil/vegetation discrimination, forest classification, man-made feature identification 2 Vegetation discrimination and health monitoring, man-made feature identification 3 Plant species identification, man-made feature identification 4 Soil moisture monitoring, vegetation monitoring, water body discrimination 5 Vegetation moisture content monitoring 6 Surface temperature, vegetation stress monitoring, soil moisture monitoring, cloud differentiation, volcanic monitoring 7 Mineral and rock discrimination, vegetation moisture content
35 TM R,G,B = 3,2,1
37 ETM+: CIR False Color Composite Image
38 TM R,G,B = 4,3,2
40 TM R,G,B = 4,5,3
42 TM R,G,B = 7,4,2
43 Tasseled Cap: Brightness
44 TC: Greenness
45 TC: Wetness
46 TC: Haziness
48 Classification Classification scheme Class signatures Unsupervised classification (ISODATA) Supervised classification Training sites Classification rules (classifiers)
49 Classification Approaches Unsupervised: self organizing Supervised: training Hybrid: self organization by categories Spectral Mixture Analysis: sub-pixel variations.
50 Clustering / Classification Clustering or Training Stage: Through actions of either the analyst s supervision or an unsupervised algorithm, a numeric description of the spectral attribute of each class is determined (a multi-spectral cluster mean signature). Classification Stage: By comparing the spectral signature to of a pixel (the measure signature) to the each cluster signature a pixel is assigned to a category or class.
51 terms Parametric = based upon statistical parameters normal distribution (mean & standard deviation) Non-Parametric = based upon objects (polygons) in feature space Decision Rules = rules for sorting pixels into classes
52 Resolution and Spectral Mixing
54 Clustering Minimum Spectral Distance - unsupervised ISODATA I - iterative S - self O - organizing D - data A - analysis T - technique A - (application) Band B Band A Band B Band A 1st iteration cluster mean 2nd iteration cluster mean
58 Classification Decision Rules If the non-parametric test results in one unique class, the pixel will be assigned to that class. if the non-parametric test results in zero classes (outside the decision boundaries) the the unclassified rule applies either left unclassified or classified by the parametric rule if the pixel falls into more than one class the overlap rule applies left unclassified, use the parametric rule, or processing order Useful fact: we aren t limited to using only raw DNs, radiance, or reflectance in our classifier. We can use ratio or difference indices, LSU fractions, spatial data (distance from some target) or any other data transformation we might think would be appropriate in the classifier. Non-Parametric parallelepiped feature space Unclassified Options parametric rule unclassified Overlap Options parametric rule by order unclassified Parametric minimum distance Mahalanobis distance maximum likelihood
59 Parallelepiped Band B B Maximum likelihood (bayesian) probability Bayesian, a prior (weights) A Band A Minimum Distance n SDxyc ci X xyi i 1 c class X xyi ci value of pixel x, y in i class mean of values in i for sample for class 2 c Band B Band A cluster mean Candidate pixel
60 Parallelepiped Classifier The minimum and maximum DNs for each class are determined and are used as thresholds for classifying the image. Benefits: simple to train and use, computationally fast Drawbacks: pixels in the gaps between the parallelepipes can not be classified; pixels in the region of overlapping parallelepipes can not be classified.
61 How it works... Parallelepiped Classifier
62 Parametric classifiers
63 How it works... Minimum Distance Classifier A centroid for each class is determined from the data by calculating the mean value by band for each class. For each image pixel, the distance in n-dimensional distance to each of these centroids is calculated, and the closest centroid determines the class. Benefits: mathematically simple and computationally efficient Drawback: insensitive to different degrees of variance in spectral response data.
64 How it works... Maximum Likelihood Classifier Max likelihood uses the variance and covariance in class spectra to determine classification scheme. It often, but not always, assumes that the spectral responses for a given class are normally distributed.
65 How it works... Maximum Likelihood Classifier We can then determine a probability that a given DN is a member of each class. The pixel is classified by using the most likely class or Other if the probability isn t over some threshold. Benefits: takes variation in spectral response into consideration. Drawbacks: computationally intensive; multimodal or non-normally distributed classes require extra care when training the classifier, if high accuracy is to be achieved.
66 Classification Systems USGS - U.S. Geological Survey Land Cover Classification Scheme for Remote Sensor Data USFW - U.S. Fish & Wildlife Wetland Classification System NOAA CCAP - C-CAP Landcover Classification System, and Definitions NOAA CCAP - C-CAP Wetland Classification Scheme Definitions PRISM - PRISM General Landcover King Co. - King County General Landcover (specific use, by Chris Pyle) Level 1 Urban or Built-Up Land 11 Residential 12 Commercial and Services 13 Industrial 14 Transportation, Communications and Utilities 15 Industrial and Commercial Complexes 16 Mixed Urban or Built-Up 17 Other Urban or Built-up Land 2 Agricultural Land 21 Cropland and Pasture 22 Orchards, Groves, Vineyards, Nurseries and Ornamental Horticultural Areas 23 Confined Feeding Operations 24 Other Agricultural Land
73 Lab 2. Decision (Classification) Tree Represented as a set of hierarchically-arranged decision rules (i.e., tree-branch-leaf) Could be generated by knowledge engineering, neural network, or statistic methods. S-Plus: Tree Models: successively splitting the data to form homogeneous subsets.
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