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Satellite Remote Sensing Data And Image Processing Using IDRISI an Image
processing and GIS Software
M.N.Reddy
PROJECT1 :
Land use/Land Cover Classification from Remote Sensing Data - IDRISI an Image processing and GIS Software
PROJECT2 : Characterization:
Characterization of Agro-climatic Zones in Mahabubnagar District in AP based on Rainfall and Temperature
IDRISI –not an Acronym
Al-Idrisi–Muslim Scholar (1100-1166)
Idrisi Project –Ron Eastman of Clark University, USA, 1987
Clark Labs –1994
COMPONENTS OF IDRISI
• Database management system (Spatial and Attribute )
• Cartographic Display System
• Geographical Analysis System
• Statistical Analysis System
• Decision Support System
• Map digitization System (CattaLinx)
• Image Processing System
Database
Display
Digitization
Geographic analysisStatistical
analysis
Decision support
Data Products
• Photographic Products
• Digital Products
•A digital image is defined as a matrix of digital numbers(DNs).
Classification of Data Products
• Raw data
• Partially corrected products
• Geo-coded Products
• Precision Products
Information extraction
• Visual Interpretation
• Classification
Image Processing:
Act of examining images for the purpose of identifying objects and judging their significance
Image Scale and Resolution
Scale : number of unit on ground units represented by a single unit on the image
Resolution: Ability of an imaging system to record fine details in a distinguishable manner
Example: Suppose you have a IRC 1D image of 1000 rows and 1000 columns. If you print this image on a paper of 100 cm by 100 cm in size, what would be the scale of the printed image?
(Resolution of IRS 1D = 23.5 m)
PROJECT1 :
Land use/Land Cover Classification from Remote Sensing Data - IDRISI an Image processing and GIS Software
1. Raw satellite data bearing scene number TRPC20026J099-059 of part of Medak ditrict in AP
Available Data
2. Scanned Survey of India Toposheets 56F11, 56F12 in 1:50,000
3. Reference Points of the Images for rectification(Geo-referencing)
1. Importing Digital data to IDRISI
2. Image Rectification/Geo- referencing
Steps
3. Rectification of Toposheets (Two)
4. Subset the Image with respect to Toposheets
5. Registration of subset Image with respect to Subset Toposheets
6. Mosaic the Images (Joining)
7. Unsupervised/Supervised Classification
8. Raster to Vector conversion
1. Create a Data Folder (working Directory)&
Setting Project Environment
Exercise Details
2. Importing Satellite Data –Band by band and Composite all 4 bands
3. Study Layer Properties – Image properties
5. Creating Imagery Group Files
6. Image Rectification with already available data in leader file –5 known Lat, lan Points
7. Importing Toposheet 5611.jpg to Idrisi Fomat
8. Rectification / Geo-referencing Toposheet
9. Subsetting the Toposheet 56F11
10. Converting Vector file of Subset toposheet to Raster
11. Importing Toposheet 56F12.jpg to Idrisi Fomat12. Rectification / Geo-referencing Toposheet 56F12
13. Subsetting the Toposheet 56F12
14. Converting Vector file of Subset toposheet to Raster
15. Overlay of Toposheet 56f11 with rectified Image9959
16. Overlay of Toposheet 56f12 with rectified Image9959
17. Composite the files –subset56f11_1 /3
18. Composite the files –subset56f12_1 /3
19. Grouping the files –subset56f11_1 /3 and subset56f12_1 /3
20. Registration of Image with reference to Toposheets
21. Rectification of clipped image with respect to Toposheet
22. Mosaic of Images
23. Classification of images
• GCP (Ground Control Points)
• Rectification
• Re-sampling
• Registration
Geometric Correction:The transformation of a Remotely Sensed image so that it has the scale and projection properties of a map is called geometric correction (Mather, 2002)
GCP: Ground Control Points:
Identification of geographical features on he image is called GCP
Positions are known as intersection of streams, highways,airport, runways etc.
Latitudes and Longitudes can be determined by accurate base maps.
Rectification: Rectification (rubber sheeting is the process of removing distortion from imagery by wrapping the image to fit map projection
Each pixel is assigned a map coordinate during rectification
Registration Is the process of making image data to confirm to another image
Resampling method:
The location of output pixels derived from the ground control points (GCPs) is used to establish the geometry of the output image and its relationship to the input image.
Difference between actual GCP location and their position in the image are used to determine the geometric tranformation
56
69
134
58
82
135
62
94
129
BAND1
BAND2
BAND3
14
156
120
197
157
172
152
143
184
BAND1
BAND2
BAND3
Six-pixel, 3-band Digital Image
Resolution Types
•Spectral resolution
•Radiometric resolution
•Spatial resolution
•Temporal resolution
Remote Sensing is the science and art of obtaining information about an object, area or phenomenon through an analysis of the data acquired by a device which is not in contact with the object, area or phenomenon under investigation.
Spatial resolution refers to the fineness of details visible in an image
Spatial resolution
It is the spatial resolution of a sensor that determines the level of spatial details that it provides about features on the Earth’s surface
Spatial resolution refers to the fineness of details visible in an image
Spectral resolution refers to the width of the spectral bands. It is the width across the electro magnetic spectrum that the
Spectral resolution
It is the spatial resolution of a sensor that determines the level of spatial details that it provides about features on the Earth’s surface
Radiometric resolution refers to the ability of a remote sensing system to record many levels of values
Radiometric resolution
It refers to the number of digital values used to express the data collected by the sensor. It is commonly expressed as the number of bits needed to store the maximum level.
Temporal resolution is the imaging revisit interval. It is the frequency with which imges of a given geographical location can be aquired
Temporal resolution
The temporal resolution is determined by orbital characteristics and swath width etc..
Data Processing and Remote sensing
Steps
• Information Extraction
• Pre-processing
• Display and enhancement
Pre-processing
• Earth rotation correction
• Noise reduction
• Radiometric Correction
• Atmospheric Correction
• Geometric correction
Image Enhancement
• Contrast Stretching
• Density Slicing
• Colour composites
• Ratio Images
• Convolution Filtering
• Principal Components
• Edge Enhancement and Linear Filtering
• Colour space Transformation
Image Classification
Is the process of identification of the patterns associated with each pixel position in an image in terms of the characteristics of the objects at the corresponding point on the Earth’s surface.
Land classification
• Aims to label each pixel in a scene to specific land cover types.
• Pixels can then be either correctly classified, incorrectly classified or unclassified.
• Two main type of classification– Unsupervised– Supervised
Un-supervised Classification is a process of grouping pixels that have similar spectral values
Each group of similar pixels is called spectral class
•No previous knowledge assumed about data.•Tries to spectrally separate the pixels.•User has controls over:
–Number of classes–Number of iterations–Convergence thresholds
•Two main algorithms: Isodata and k-means
Example : spectral plot
Band 1
Ban
d 2
• Two bands of data.
• Each pixel marks a location in this 2d spectral space
• Our eye’s can split the data into clusters.
• Some points do not fit clusters.
Example k-means
Band 1
Ban
d 2
Band 1
Ban
d 2
Band 1
Ban
d 2
1. First iteration. The cluster centres are set at random. Pixels will be assigned to the nearest centre.
2. Second iteration. The centres move to the mean-centre of all pixels in this cluster.
3. N-th iteration. The centres have stabilised.
ISODATA (unsupervised)
• Extends k-means. Also calculate standard deviation for clusters.
• After stage 3 we can either:– Combine clusters if centres are close.– Split clusters with large standard
deviation in any dimension.– Delete clusters that are to small.
• Then reclassify each pixel and repeat.• Stop on max iterations or convergence
limit.• Assign class types to spectral clusters.
Example ISODATA
Band 1
Ban
d 2
1. Data is clustered but blue cluster is very stretched in band 1.
2.Cyan and green clusters only have 2 or less pixels. So they will be removed.
Band 1
Ban
d 2
Band 1
Ban
d 2
3. Either assign outliers to nearest cluster, or mark as unclassified.
Supervised Classification
Is to sample areas of known cover types to determine representative spectral values of each cover type.
The samples are referred – Training
fields
Representative spectral values – Spectral
signatures
Supervised Classification Procedures
• Maximum-likelihood Method
• Discriminant Function
• Bayesian Method
• Parallelepiped
• Minimum distance
• Neural network
Parallelepiped (supervised)
• For each training region determine the range of values observed in each band.
• These ranges form a spectral box (or parallelepiped) which is used to classify this class type.
• Assign new image pixels to the parallelepiped which it fits into best.
• Pixels outside all boxes can be unclassified or assigned to the closest one.
• Problems with classes that exhibit high correlation between bands. This creates long ‘diagonal’ data-sets that don’t fit well into a box.
Parallelepiped example
Training classes plotted in spectral space. In this example using 2 bands.
Maximum likelihood (supervised)
• For each training class the spectral variance and covariance is calculated.
• The class can then be statistically modelled with a mean vector and covariance matrix.
• This assumes the class is normally distributed. Which is generally okay for natural surfaces.
• Unidentified pixels can then be given a probability of being in any one class.
• Assign the new pixel to the class with the highest probability – or unclassified if all probabilities low.
Concept of Maximum Likelihood Classification
Maximum likelihood example
Equiprobability contours
• Normal probability distributions are fitted to each training class.
• The lines in the diagram show regions of equal probability.
• Point 1 would be assigned to class ‘pond culture’ as this is most probable.
• Point 2 would generally be unclassified as the probabilities of fitting into one for the classes would be below threshold.
1
2
Problem:
Some people recommend rectifying an image after it has been classified. The argument is
1) Rectification is quicker since each pixel contains only one class value instead of many spectral values.
2) Some spectral integrity is lost during the pixel resembling process – an un-rectified image is is spectrally more correct than a rectified image.
If you do not agree give the reasons to rectify an image for before classification ,say, vegetation mapping.