• 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.
Jensen, 2004Jensen, 2004
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.
Jensen, 2004Jensen, 2004
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.
Jensen, 2005Jensen, 2005
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.
Jensen, 2005Jensen, 2005
Jensen, 2005Jensen, 2005
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.