International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 3, March 2013
ISSN 2319 - 4847
Image Analysis based on Spectral and Spatial
Grouping
B. Naga Jyothi1, K.S.R. Radhika2 and Dr. I. V.Murali Krishna3
1
2
Assoc. Prof., Dept. of ECE, DMS SVHCE, Machilipatnam, A.P., India
Assoc. Prof., Dept. of CSE, DMS SVHCE, Machilipatnam, A.P., India
3
Director (Retd.,), IST/R&D Center, JNTU, Hyderabad, India.
ABSTRACT
The paper aims to present image classification using region wise information. Effort is made to use spectral labelled &
classified image as well as spatial information as the basis for classification .Using a selected sample set , the image is first
divided into several groups in the spectral space using mahalanobis distance as a measure of similarity .The output is then
segmented by growing pixels with equal IDs basing on some specific connectivity. The entire image information is then
available as region information. Each region is defined by a no. of attributes such as unique ID , size ,the list of all its member
pixels ,the mean intensity and covariance matrix of the spectral values in that region. A structure is used to hold the
segmentation information and the entire data is saved as a file. Secondly, a region labelled image consisting of region IDs is
created. Finally the regions are classified using Euclidean distance measure.
Keywords: Spectral Classification, Region Labelling, Euclidean Distance, Training Samples
1. INTRODUCTION
Depending on the image primitive used viz, pixel or object based, image classification methods are of two main
categories. Pixel based classification methods use only the spectral patterns to classify the individual pixels. Object
based methods try to group pixels into objects using an image segmentation process based on some similarity chosen
and then use both the spectral, spatial and contextual information of these objects to classify the whole image. Object
based methods eliminates mixed pixel problem suffered by most pixel based methods and hence are superior ways of
image classification.
1.1 SUPERVISED CLASSIFICATION
In supervised classification, the identity of land cover types are known prior through some means such as aerial
photography, map analysis and personal experience etc. The analyst locates specific sites that represent homogeneous
examples in the remotely sensed data. These areas are referred as training samples and the spectral characteristics of
these areas are used to train the classification algorithm of the remainder of the image. Multivariate statistical
parameters such as mean, standard deviation, covariance matrices, correlation matrices 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.
Thematic classification of an image involves the following steps: [Ref: 3]
• Feature extraction: Transformation of the multispectral image by a spatial or spectral transform to a feature image.
Examples are selection of subset of bands, a PCT to reduce the data dimensionality, or a spatial smoothing filter. This
step is optional i.e., the multispectral image can be used directly, if desired
• Training: Selection of pixels to train the classifier to recognize the different themes, or classes, and determination of
decision boundaries which partition the feature space according to the training pixel properties. This step is either
supervised by the analyst or unsupervised with the aid of a computer algorithm. For supervised training, the analyst
must select representative pixels for each of the categories. It is important that the training area be a homogenous
sample of the respective class, but at the same time include the range of variability for the class.
• Labelling: Application of the feature space decision boundaries to the entire image to label all the pixels. If the
training was supervised, the labels are already associated with the feature space regions; if it was unsupervised, the
analyst must now assign labels to the regions. The output map consists of one label for each pixel.
1.2 INPUT IMAGE DETAILS
The input image [Ref.1: www.nasa.gov] is Landsat 7 pan image of Pyongyang, North Korea acquired in September
2002. [Fig:1] This is a natural-color image using ETM+ bands 3, 2, 1. In this image, Pyongyang appears grey in color
Volume 2, Issue 3, March 2013
Page 486
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 3, March 2013
ISSN 2319 - 4847
and is surrounded by vegetation (in green). The Taedong River, shown in dark blue, almost black travels through the
city.
Fig: 1 Input Image
2. METHODOLOGY
2.1 SPECTRAL CLASSIFICATION [ Ref.2 Lonesome M. Malambo 2009].
Spectral grouping is done by determining the closeness of each image pixel to each of the samples selected from the
input image. Samples are selected based on the expected number of classes in the image and are assigned different IDs.
Each pixel is assigned to its closest sample ID based on Mahalanobis distance measure of closeness.
The Mahalanobis distance D as defined below, is used a measure of closeness or similarity.
…. (1)
In (1), x is the pixel spectral vector, μ is the mean spectral vector of a sample in a multi band image, Σ is the covariance
matrix of the sample, T denotes the transpose of the matrix. The output is an image composed of sample IDs.
2.2 IMAGE SEGMENTATION
Spatial grouping uses the image created from the spectral grouping as the input. Equal IDs are grown in the region
growing process with specific neighbor connectivity. A structure is used to hold the segmented image information.
Each region is defined by an unique ID, list of its member pixels, mean and covariance of intensities in the region.
Finally a region label image comprising of region IDs is obtained . The region wise information can be saved to a file
for further processing.
2.3 IMAGE CLASSIFICATION
Specific regions are selected to serve as training samples for region classification. Regions are classified using
Euclidean distance. Each region is compared to the training samples and is assigned to the closest class.
3. IMPLEMENTATION
The programming uses MATLAB 7.0.1 [Ref.2 Lonesome M. Malambo 2009].
The sequence of steps involved in the developed code are as follows:
 The loaded input image is smoothed using a Gaussian filter to reduce noise.
 Samples are selected from different classes Viz., vegetation, water, settlement and barren regions [Fig:2] and
their statistics are computed [ Table:1 ]
 The samples are Labelled and Mahalanobis distance is calculated between each pixel and each instance of
sample set. Pixels close to a sample are assigned the same sample ID.
 The result is an image composed of sample IDs [Fig:3 spectral classified image]
 Region growing is done with only equal ID pixels using 8-neighbor connectivity.
 Segmented information regarding individual regions is loaded into a structure. Each region has its own ID,
member pixels, mean and covariance of intensities in it. [ Table: 2 ]
 A region label image with region IDs is formed. Then segmented image is observed [Fig:4].
 For region classification ,training Samples are selected from different Regions Viz., vegetation, water,
settlement and barren regions using MATLAB’s getpts function
 An image region is classified based on its Euclidean distance to a training sample. Regions close to a sample
are assigned the same sample ID.
Volume 2, Issue 3, March 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 3, March 2013
ISSN 2319 - 4847
 Finally the result is a region (object) classified image . [Fig:5].
4. RESULTS
Sample 1: Vegetation
Fig: 2 Selected samples from different classes in the input image
Sample 2: water
Sample 3: Settlement
Sample 4: Barren
Vegetation: dark blue
Fig: 3 Output of Spectral Classification
Water: light blue
Settlement: yellow
Table: 1
Volume 2, Issue 3, March 2013
Barren: red
Sample statistics for spectral Classification
Page 488
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 3, March 2013
ISSN 2319 - 4847
Vegetation: yellow
Fig: 4 Output of region growing process & segmented image
Water: light blue
Settlement: dark blue
Barren: orange
Table: 2 Sample set of Region wise information
Fig: 5 Output of region classification process & Final region (object) classified image
Vegetation: light blue
Water: dark blue
Settlement: yellow
Volume 2, Issue 3, March 2013
Barren: red
Page 489
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 3, March 2013
ISSN 2319 - 4847
5. CONCLUSION
Good results have been obtained as can be seen by comparing the input and output classified images. This is because
the image is classified on the region (object) level and usually more information is used for classification .The final
classified result is influenced by the samples considered in the spectral grouping , final classification process and also
the number of samples taken for each class.
REFERENCES
[1] www.nasa.gov + NASA Home Multimedia. http://landsat.gsfc.nasa.gov/images/archive/c0018.html
[2] Lonesome M.Malambo “A region based Approach to Image Classification”, Applied Geoinformatics for Society
and
Environment 2009-Stuttgart University of Applied Sciences.
[3] Robert A. Schowengerdt. “Remote Sensing: Models and Methods for Image Processing” (3rd Ed.). Academic
Press USA, pg: 388,396.
[4] John R. Jensen “Introductory Digital Image Processing”, A Remote Sensing Perspective, Third Edition, PPH
[5] B. Naga Jyothi, Dr. G. R. Babu, Dr. I. V. Murali Krishna, “Thematic classification of multispectral imagery,
International Journal of Electronics and computer Science Engineering , V1N2-181-190, 2012
[6] KSR Radhika, B.Naga Jyothi, et.al.“Accuracy Assessment of per pixel Based Classification” Conference
Proceedings, NC-Velasiem-2k12: Pg.73-77
[7] MathWorks.Image Processing Toolbox 7.0.1. Mathworks.com
[8] D. LU and Q. WENG “A survey of image classification methods and techniques for improving classification
performance” International Journal of Remote Sensing Vol. 28, No. 5, 823–870, 10 March 2007
ACKNOWLEDGEMENTS
The authors acknowledge "Landsat imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological
Survey" or "USGS/NASA Landsat" for the images.
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