Remote Sensing Image Processing and Interpretation

Name: Remote Sensing Image Processing and Interpretation
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Description: Remote Sensing Image Processing and Interpretation

Remote Sensing Image Processing and Interpretation
------Using GIS-- Introduction to GIS Lecture 22: Remote Sensing Image Processing and Interpretation By Austin Troy University of Vermont

Introduction to GIS Image Pre-Processing Once an image is acquired it is generally processed to eliminate errors Geometric correction Radiometric correction It is also “enhanced” to make it more viewable ©2005 Austin Troy

Image enhancement For improving image quality, particularly contrast
Introduction to GIS Image enhancement For improving image quality, particularly contrast Includes a number of methods used for enhancing subtle radiometric differences so that the eye can easily perceive them Two types: point and local operations Point: modify brightness value of a given pixel independently Local: modify pixel brightness based on neighborhood brightness values ©2005 Austin Troy

Contrast enhancement (point operation)
Introduction to GIS Contrast enhancement (point operation) Most images start with low contrast; these improve it Level slicing reclasses DNs into fewer classes, so differences can be more easily seen; colors or grayscale values can be assigned. Like resampling down radiometric resolution. Often used where histogram shows bimodal distribution of reflectance values Contrast Streching is the opposite, where a smaller number of values are stretched out over full DN range ©2005 Austin Troy

Contrast enhancement Here is what spectral histograms look like
Introduction to GIS Contrast enhancement Here is what spectral histograms look like Note that DN is not zero for any of them ©2005 Austin Troy Source:

Introduction to GIS Contrast enhancement Gray level thresholding: all pixel values below a lower threshold are mapped to zero and those above an upper threshold are mapped to 255. All other pixel values are linearly interpolated to lie between 0 and 255 Grey-Level Transformation Table for performing linear grey level stretching of the three bands of the image. Red line: XS3 band; Green line: XS2 band; Blue line: XS1 band. ©2005 Austin Troy Source:

Contrast enhancement Introduction to GIS
The image on the left is hazy because of atmospheric scattering; the image is improved (right) through the use of Gray level thresholding. Note that there is more contrast and features can be better discerned ©2005 Austin Troy Source:

Spatial Feature Enhancement (local operation)
Introduction to GIS Spatial Feature Enhancement (local operation) Spatial filtering/ Convolution: neighborhood operations (like we reviewed for raster analysis), that calculate a new value for the center pixel based on the values of its neighbors within a window (see “More Raster Analysis” lecture for more); includes low-pass (emphasizes regional spatial trends, demphasizes local variability ) and high-pass (emphasizes local spatial variability) filters ©2005 Austin Troy

Spatial Feature Enhancement
Introduction to GIS Spatial Feature Enhancement Edge Enhancement: This is a convolution method that combines elements of both low and high-pass filtering in a way that accentuates linear and local contrast features without losing the regional patterns First, a high-pass image is made with local detail Next, all or some of the gray level of the original scene is added back Finally, the composite image is contrast stretched ©2005 Austin Troy

Classification of Imagery
Introduction to GIS Classification of Imagery Classification can be used for numerous purposes, like classifying geology, water temperature, soil moisture, other soil characteristics, water sediment load, water pollution levels, lake eutrophication, flood damage estimation, groundwater location, vegetative water stress, vegetative diseases and stresses, crop yields and health, biomass quantity, net primary productivity, forest vegetation species composition, forest fragmentation, forest age (in some cases), rangeland quality and type, urban mapping and vectorization of manmade structures One of the most common applications of classification is land cover and land use mapping ©2005 Austin Troy

Land Cover/ Land Use Mapping
Introduction to GIS Land Cover/ Land Use Mapping Land cover refers to the feature present and land use refers to the human activity associated with a plot of land The LU/LC classes to be derived will depend on the system being used. One of the most common is the USGS Anderson Classification System (Anderson et al. 1976). This classification scheme is hierarchical, with nine very general categories at Level I, and an increasing number of classes and detail and level increases. Paper available online at Anderson system intermixes land use and land cover metrics, by inferring land use from land cover. Unfortunately, land cover can only tell us a limited amount about land use—think of outdoor recreation as a land use. Need additional data for these classes. ©2005 Austin Troy

Introduction to GIS Land Cover/ Land Use Mapping Land use and land cover classification system for use with remote sensor data (Anderson et al. 1976) Level I Level II 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 Land 17 Other Urban or Built-up Land ©2005 Austin Troy

Introduction to GIS Land Cover/ Land Use Mapping Level I Level II 2 Agricultural Land 21 Cropland and Pasture 22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas Confined Feeding Operations 24 Other Agricultural Land 3 Rangeland 31 Herbaceous Rangeland 32 Shrub and Brush Rangeland 33 Mixed Rangeland 4 Forest Land 41 Deciduous Forest Land 42 Evergreen Forest Land 43 Mixed Forest Land 5 Water Streams and Canals 52 Lakes 53 Reservoirs 54 Bays and Estuaries 6 Wetland Forested Wetland 62 Nonforested Wetland ©2005 Austin Troy

Introduction to GIS Land Cover/ Land Use Mapping Level I Level II 7 Barren Land 71 Dry Salt Flats. 72 Beaches 73 Sandy Areas other than Beaches 74 Bare Exposed Rock 75 Strip Mines Quarries, and Gravel Pits 76 Transitional Areas 77 Mixed Barren Land 8 Tundra Shrub and Brush Tundra 82 Herbaceous Tundra 83 Bare Ground Tundra 84 Wet Tundra 85 Mixed Tundra 9 Perennial Snow or Ice 91 Perennial Snowfields 92 Glaciers ©2005 Austin Troy

Introduction to GIS Land Cover/ Land Use Mapping Level 3 and 4 categories deliver even more detail. USGS only specifies classifications for 1 and 2. They suggest that higher level classification be designed by local planners who know the land uses, because of the narrowness of the categories As an example for level 3, with “urban” (level 1) “residential” (level 2) category, includes single family home (111), multifamily home (112), group quarters (113), mobile home parks (115), etc. LANDSAT data can be used to generate level 1 easily, level 2 with some finesse (15 to 20 m resolution recommended) Levels 3 and 4, IKONOS data or aerial photographs are needed. Level 4 requires much supplemental information ©2005 Austin Troy

Introduction to GIS Image classification This is the science of turning RS data into meaningful categories representing surface conditions or classes Spectral pattern recognition procedures classifies a pixel based on its pattern of radiance measurements in each band: more common and easy to use Spatial pattern recognition classifies a pixel based on its relationship to surrounding pixels: more complex and difficult to implement Temporal pattern recognition: looks at changes in pixels over time to assist in feature recognition ©2005 Austin Troy

Spectral Classification
Introduction to GIS Spectral Classification Two types of classification: Supervised: the analyst designates on-screen “training areas” known land cover type from which an interpretation key is created, describing the spectral attributes of each cover class . Statistical techniques are then used to assign pixel data to a cover class, based on what class its spectral pattern resembles. Unsupervised:automated algorithms produce spectral classes based on natural groupings of multi-band reflectance values (rather than through designation of training areas), and the analyst uses references data, such as field measurements, DOQs or GIS data layers to assign areas to the given classes ©2005 Austin Troy

Introduction to GIS Spectral Classification Unsupervised: Computer groups all pixels according to their spectral relationships and looks for natural spectral groupings of pixels, called spectral classes Assumes that data in different cover class will not belong to same grouping Once created, the analyst assesses their utility Spectral class 1 Spectral class 2 Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation. ©2005 Austin Troy

Introduction to GIS Spectral Classification Unsupervised: After comparing the reclassified image (based on spectral classes) to ground reference data, the analyst can determine which land cover type the spectral class corresponds to Has advantage over supervised classification: the “classifier” identifies the distinct spectral classes, many of which would not have been apparent in supervised classification and, if there were many classes, would have been difficult to train all of them. Not required to make assumptions of what all the cover classes are before classification. Clustering algorithms include: K-means, texture analysis ©2005 Austin Troy

Introduction to GIS Spectral Classification Supervised: Better for cases where validity of classification depends on a priori knowledge of the technician Conventional cover classes are recognized in the scene from prior knowledge or other GIS/ imagery layers Therefore selection of classes is pre-determined and supervised Training sites are chosen for each of those classes Each training site “class” results in a cloud of points in n dimensional “measurement space,” representing variability of different pixels spectral signatures in that class ©2005 Austin Troy

Introduction to GIS Spectral Classification Supervised: The next step is for the computer to assign each pixel to the spectral class is appears to belong to, based on the DN’s of its constituent bands There are numerous algorithms the computer uses, including: Minimum distance to means classification (Chain Method) Gaussian Maximum likelihood classification Parallelpiped classification ©2005 Austin Troy

Introduction to GIS Spectral Classification Supervised: These algorithms look at “clouds” of pixels in spectral “measurement space” from training areas, and try to determine which “cloud” a given non-training pixel falls in. The simplest method is “minimum distance” in which a theoretical center point of point cloud is plotted, based on mean values, and an unknown point is assigned to the nearest of these. That point is then assigned that cover class. They get much more complex from there. ©2005 Austin Troy

Object-Oriented Classification
Introduction to GIS Object-Oriented Classification Traditional classifiers don’t work as well for new generation of high resolution data, like this 2 foot Emerge Color infrared airphoto. Why? Meaningless to classify each pixel ©2005 Austin Troy

Object-oriented classification Steps:
Introduction to GIS Object-oriented classification Steps: Segmentation of rasters into polygon objects Objects are defined such that they minimize within-unit heterogeneity and maximize between unit heterogeneity, subject to some user defined parameters. The user can control the scale parameter for acceptable level of heterogeneity. They can also control the degree to which segmentation is based on spectral or spatial characteristics, since heterogeneity is defined in terms of both. By repeating the segmentation with different scale parameters, the user can create a nested hierarchy of objects>>big objects containing smaller objects, containing smaller objects ©2005 Austin Troy

Introduction to GIS Object-oriented classification Steps: Following segmentation, each object is encoded with information about its tone, shape, area, context, neighborhors and spectral characteristics (e.g. mean, standard deviation, max, min or each band’s spectral reflectance) This information can be used for feature extraction in which objects’ properties are analyzed to look for characteristics that help to discriminate one object type from another. That is, what object information helps discriminate one from another? ©2005 Austin Troy

Introduction to GIS Object-oriented classification Steps: Then objects are classified by either defining training areas of known cover type (known as supervised fuzzy classification) or creating class descriptions organized through inheritance-based rules into a knowledge base (known as fuzzy knowledge base classification). ©2005 Austin Troy

Introduction to GIS Object-oriented classification Steps: In the knowledge base approach, complex membership functions can be derived that describe characteristics that are typical or atypical for a certain class. The more a given object displays the characteristics, the more likely it is to be classified into the class to which those characteristics pertain. Characteristics can be based on spectral response summary statistics, shape characteristics, adjacency, connectivity, and overlay with certain thematic features. ©2005 Austin Troy

Object-oriented classification Steps
Introduction to GIS Object-oriented classification Steps The classification can be hierarchical and nested, with finer classifications within coarser ones Small classified objects can be aggregated up to large object classes and large objects can be split into smaller ones. Can then assign different segmentations to different class hierarchy level Allows for high precision classifications within coarser, general classifications ©2005 Austin Troy

Introduction to GIS Object-oriented classification Steps Can use additional thematic layers to populate the knowledge base and create rules about what a certain class can be on top of, next to, or near. This can increase the accuracy of classifications, especially as you increase categorical precision and start getting into classifiying land uses in addition to land cover Hence, when you do training areas, you not only get average spectral responses and shape metrics for a class, but also can get average values from underlying layers to help increase classification accuracy Examples: farm fields as fn of slope, soils, etc; different suburban development types as function of distance to urban centers, income, crime, etc. ©2005 Austin Troy

Object-oriented classification Software
Introduction to GIS Object-oriented classification Software eCognition: one of the top Object oriented classification software packages More info: see Ecognition website: ©2005 Austin Troy

Introduction to GIS Accuracy Assessment This is one of the most important parts of image classification. Error rates can be very high in classification accuracies, especially with lower resolution data, and where pixels are mixed This is often the most time consuming part of image classification NLCD effort undertook effort to classify errors in each type of land cover, broken down by region of the US User’s accuracy for type X: Percent of pixels classified as X (e.g. “forest”) that really are forest (measures errors of commission). Producer’s accuracy: percent of pixels that were classified as other than forest but really are forest (measures errors of omission). ©2005 Austin Troy

Accuracy Assessment:example
Introduction to GIS Accuracy Assessment:example Table 1 Accuracy Assessment of LULC classification Classified data Reference data Sum User Acc. % Urban Forest Other 71 13 16 100 2 10 88 73 123 104 300 Producer Acc. (%) 97.3 81.3 84.6 Overall accuracy (%) ©2005 Austin Troy

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