Imageprocessing-idrisi-feb2008

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 • Cartographic Display System • Map digitization System (CattaLinx) • Database management system (Spatial and Attribute ) • Geographical Analysis System • Statistical Analysis System • Image Processing System • Decision Support System

Display

Decision support

Digitization Database

Statistical analysis

Geographic analysis

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 Available Data 1. Raw satellite data bearing scene number TRPC20026J099-059 of part of Medak ditrict in AP 2. Scanned Survey of India Toposheets 56F11, 56F12 in 1:50,000 1. Reference Points of the Images for rectification(Geo-referencing)

Steps 1. Importing Digital data to IDRISI 2. Image Rectification/Georeferencing 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

Exercise Details 1. Create a Data Folder (working Directory)& Setting Project Environment 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 Fomat 12. 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

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) • Rectification • Re-sampling • Registration

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

Six-pixel, 3-band Digital Image 56 69 134

58 82 135

62 94 129

BAND1 BAND2 BAND3

14 156 120

197 157 172

152 143 184

BAND1 BAND2 BAND3

Resolution Types •Spatial resolution •Spectral resolution •Radiometric 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 Spatial resolution refers to the fineness of details visible in an image 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

Spectral resolution Spectral resolution refers to the width of the spectral bands. It is the width across the electro magnetic spectrum that the 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 Radiometric resolution refers to the ability of a remote sensing system to record many levels of values 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 Temporal resolution is the imaging revisit interval. It is the frequency with which imges of a given geographical location can be aquired The temporal resolution is determined by orbital characteristics and swath width etc..

Data Processing and Remote sensing

Steps • Pre-processing • Display and enhancement • Information Extraction

Pre-processing • Earth rotation correction • Noise reduction • Radiometric Correction • Atmospheric Correction • Geometric correction

Image Enhancement • Contrast Stretching • Density Slicing • Colour composites • Ratio Images • Principal Components • Convolution Filtering • 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 •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 Each group of similar pixels is called spectral class

Example : spectral plot • Two bands of data.

Band 2

• Each pixel marks a location in this 2d spectral space • Our eye’s can split the data into clusters. Band 1

• Some points do not fit clusters.

Band 1

1. First iteration. The cluster centres are set at random. Pixels will be assigned to the nearest centre.

Band 2

Band 2

Band 2

Example k-means

Band 1

2. Second iteration. The centres move to the mean-centre of all pixels in this cluster.

Band 1

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.

Band 2

Band 2

Band 2

Example ISODATA

Band 1

Band 1

Band 1

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.

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 • 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

Equiprobability contours 2

Problem: Some people recommend rectifying an image after it has been classified. The argument is 3) Rectification is quicker since each pixel contains only one class value instead of many spectral values. 4) 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.