• Major Operations of Digital Image Processing (DIP)• Image Quality Assessment• Radiometric Correction• Geometric Correction• Image Classification

Introduction to Digital Image Processing

• Image correction and restoration (radiometric and geometric principally)• Image enhancement• Image classification• Data-set merging• Modeling

Five Major Operations of DIP

Histogram of A Single Band of

Landsat Thematic Mapper Data of Charleston, SC

Histogram of A Single Band of

Landsat Thematic Mapper Data of Charleston, SC

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Cursor and Raster Display of Brightness Values Cursor and Raster Display of Brightness Values

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Two- and Three-Dimensional

Evaluation of Pixel Brightness Values

within a Geographic Area

Two- and Three-Dimensional

Evaluation of Pixel Brightness Values

within a Geographic Area

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Common Symmetric and

Skewed Distributions in

Remotely Sensed Data

Common Symmetric and

Skewed Distributions in

Remotely Sensed Data

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Line-start ProblemsLine-start Problems

N-line StripingN-line Striping

• Image Correction– Radiometric correction – corrects for brightness variations– Geometric correction – aligns image with map or coordinate system

• Image Enhancement – exaggerates brightness values, improving the detection of objects• Image Classification – groups unidentified elements (pixels) into discrete classes

Digital Processing Operations

Geometric Correction Geometric Correction

It is usually necessary to preprocess remotely sensed data and remove geometric distortion so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing–derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.

It is usually necessary to preprocess remotely sensed data and remove geometric distortion so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing–derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.

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a) Landsat satellites 4, 5, and 7 are in a Sun-synchronous orbit with an angle of inclination of 98.2. The Earth rotates on its axis from west to east as imagery is collected. b) Pixels in three hypothetical scans (consisting of 16 lines each) of Landsat TM data. While the matrix (raster) may look correct, it actually contains systematic geometric distortion caused by the angular velocity of the satellite in its descending orbital path in conjunction with the surface velocity of the Earth as it rotates on its axis while collecting a frame of imagery. c) The result of adjusting (deskewing) the original Landsat TM data to the west to compensate for Earth rotation effects. Landsats 4, 5, and 7 use a bidirectional cross-track scanning mirror.

a) Landsat satellites 4, 5, and 7 are in a Sun-synchronous orbit with an angle of inclination of 98.2. The Earth rotates on its axis from west to east as imagery is collected. b) Pixels in three hypothetical scans (consisting of 16 lines each) of Landsat TM data. While the matrix (raster) may look correct, it actually contains systematic geometric distortion caused by the angular velocity of the satellite in its descending orbital path in conjunction with the surface velocity of the Earth as it rotates on its axis while collecting a frame of imagery. c) The result of adjusting (deskewing) the original Landsat TM data to the west to compensate for Earth rotation effects. Landsats 4, 5, and 7 use a bidirectional cross-track scanning mirror.

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Image Skew Image Skew

a) U.S. Geological Survey 7.5-minute 1:24,000-scale topographic map of Charleston, SC, with three ground control points identified (13, 14, and 16). The GCP map coordinates are measured in meters easting (x) and northing (y) in a Universal Transverse Mercator projection. b) Unrectified 11/09/82 Landsat TM band 4 image with the three ground control points identified. The image GCP coordinates are measured in rows and columns.

a) U.S. Geological Survey 7.5-minute 1:24,000-scale topographic map of Charleston, SC, with three ground control points identified (13, 14, and 16). The GCP map coordinates are measured in meters easting (x) and northing (y) in a Universal Transverse Mercator projection. b) Unrectified 11/09/82 Landsat TM band 4 image with the three ground control points identified. The image GCP coordinates are measured in rows and columns.

Min-Max Contrast Stretch

Min-Max Contrast Stretch

20042004

+1 Standard Deviation Contrast Stretch

+1 Standard Deviation Contrast Stretch

Contrast Stretch of Charleston, SC Landsat

Thematic Mapper Band 4 Data

Contrast Stretch of Charleston, SC Landsat

Thematic Mapper Band 4 Data

20042004

OriginalOriginal

Minimum-maximum

Minimum-maximum

+1 standard deviation

+1 standard deviation

Contrast Stretching of Charleston, SC Landsat Thematic Mapper Band 4 DataContrast Stretching of Charleston, SC

Landsat Thematic Mapper Band 4 Data

20042004

Specific percentage linear contrast stretch designed to highlight

wetland

Specific percentage linear contrast stretch designed to highlight

wetland

Histogram Equalization Histogram Equalization

Histogram Equalization Histogram Equalization

• evaluates the individual brightness values in a band of imagery and assigns  approximately  an  equal  number  of pixels  to  each  of  the  user-specified  output  gray-scale classes (e.g., 32, 64, and 256).

• applies the greatest contrast enhancement to the most populated range of brightness values in the image.

• reduces the contrast in the very light or dark parts of the image associated with the tails of a normally distributed histogram.

• evaluates the individual brightness values in a band of imagery and assigns  approximately  an  equal  number  of pixels  to  each  of  the  user-specified  output  gray-scale classes (e.g., 32, 64, and 256).

• applies the greatest contrast enhancement to the most populated range of brightness values in the image.

• reduces the contrast in the very light or dark parts of the image associated with the tails of a normally distributed histogram.

Supervised Classification Supervised Classification

In a supervised classification, the identity and location of some of the land-cover types (e.g., urban, agriculture, or wetland) are known a priori through a combination of fieldwork, interpretation of aerial photography, map analysis, and personal experience. The analyst attempts to locate specific sites in the remotely sensed data that represent homogeneous examples of these known land-cover types. These areas are commonly referred to as training  sites because the spectral characteristics of these known areas are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Multivariate statistical parameters (means, standard deviations, covariance matrices, correlation matrices, etc.) are calculated for each training site. Every pixel both within and outside the training sites is then evaluated and assigned to the class of which it has the highest likelihood of being a member.

In a supervised classification, the identity and location of some of the land-cover types (e.g., urban, agriculture, or wetland) are known a priori through a combination of fieldwork, interpretation of aerial photography, map analysis, and personal experience. The analyst attempts to locate specific sites in the remotely sensed data that represent homogeneous examples of these known land-cover types. These areas are commonly referred to as training  sites because the spectral characteristics of these known areas are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Multivariate statistical parameters (means, standard deviations, covariance matrices, correlation matrices, etc.) are calculated for each training site. Every pixel both within and outside the training sites is then evaluated and assigned to the class of which it has the highest likelihood of being a member.

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Unsupervised Classification Unsupervised Classification

In an unsupervised  classification, the identities of land-cover types to be specified as classes within a scene are not generally known  a  priori  because ground reference information is lacking or surface features within the scene are not well defined. The computer is required to group pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria. The analyst then re-labels and combines the spectral clusters into information classes.

In an unsupervised  classification, the identities of land-cover types to be specified as classes within a scene are not generally known  a  priori  because ground reference information is lacking or surface features within the scene are not well defined. The computer is required to group pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria. The analyst then re-labels and combines the spectral clusters into information classes.

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Unsupervised Classification Unsupervised Classification

Unsupervised classification (commonly referred to as clustering) is an effective method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information. Compared to supervised classification, unsupervised classification normally requires only a minimal amount of initial input from the analyst. This is because clustering does not normally require training data.

Unsupervised classification (commonly referred to as clustering) is an effective method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information. Compared to supervised classification, unsupervised classification normally requires only a minimal amount of initial input from the analyst. This is because clustering does not normally require training data.

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Unsupervised Classification Unsupervised Classification

Unsupervised classification is the process where numerical operations are performed that search for natural groupings of the spectral properties of pixels, as examined in multispectral feature space. The clustering process results in a classification map consisting of m spectral classes. The analyst then attempts a posteriori (after the fact) to assign or transform the spectral classes into thematic information classes of interest (e.g., forest, agriculture). This may be difficult. Some spectral clusters may be meaningless because they represent mixed classes of Earth surface materials. The analyst must understand the spectral characteristics of the terrain well enough to be able to label certain clusters as specific information classes.

Unsupervised classification is the process where numerical operations are performed that search for natural groupings of the spectral properties of pixels, as examined in multispectral feature space. The clustering process results in a classification map consisting of m spectral classes. The analyst then attempts a posteriori (after the fact) to assign or transform the spectral classes into thematic information classes of interest (e.g., forest, agriculture). This may be difficult. Some spectral clusters may be meaningless because they represent mixed classes of Earth surface materials. The analyst must understand the spectral characteristics of the terrain well enough to be able to label certain clusters as specific information classes.

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Grouping (labeling) of the original 20 spectral clusters into information classes. The labeling was performed by analyzing the mean vector locations in bands 3 and 4.

Grouping (labeling) of the original 20 spectral clusters into information classes. The labeling was performed by analyzing the mean vector locations in bands 3 and 4.