Image Classification (ICIIT 2010)
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


Image Classification (ICIIT 2010)

Uploaded on

M. Zaman Ali, Imran Sarwar Bajwa, M. Abbas Choudhary, M. Shahid Naweed, [2010], “Image Classification for Zone Identification in Satellite Imagery”, IEEE International Conference on Intelligence......

M. Zaman Ali, Imran Sarwar Bajwa, M. Abbas Choudhary, M. Shahid Naweed, [2010], “Image Classification for Zone Identification in Satellite Imagery”, IEEE International Conference on Intelligence and Information Technology (ICIIT 2010), Lahore Pakistan, pp:130-135

More in: Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 8 5 2 1

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Image Classification for Zone Identification in Satellite Imagery Muhammad Zaman Ali1, Imran Sarwar Bajwa2, M. Shahid Naweed1, M. Abbas Choudhary3 1 Department of Computer Science & IT, The Islamia University of Bahawalpur, Pakistan 2 School of Computer Science, University of Birmingham, England 3 College of EME, NUST, Rawalpindi, Pakistan,,, – In satellite imagery, low and high pressure zones of pressure zone identification that not only provide accurateare very critical for weather predictions. An automatic results but also improves weather forecasting affectivity.recognition system has been introduced here, which typically Following section presents a brief overview of low and highdistinguishes the low and high pressure zones in a satellite pressure zones and their key special attributes that will beimage to predict the precipitation. Principal Component used in their identification.Analysis (PCA) algorithm has been used for identification ofpressure zones where PCA is typically an image processing A. Low Pressure Zonetechnique. The system works in two phases. In first phase, A low pressure zone is a particular area in a satelliteNOAA satellite images are used to train the system. In training image that represents subsistence of pressure at the lowestphase, system identifies and extracts key special features of the level with respect to its surrounded atmosphere [1]. Byinput images to produce an image space. While, the testing moving away from the center of the low pressure zone in anyphase is employed to compare the low and high pressure zones horizontal direction, the pressure will gradually increase. Inthose are extracted from the satellite image. Identification of other words, a low pressure zone can be surrounded by solow and high pressure zones in the satellite images can help inbetter prediction of the forthcoming weather conditions i.e. many high pressure zones. [6]Low pressure zones cause cloudy and overcast weather andhigh pressure zones are predictors of a dry weather. Keywords – Image processing, Satellite Images, PrincipalComponents, PCA, Eigenvectors I. INTRODUCTION Clouds are the basic elements of rain. There are so manysources of clouds formation. One of the few reasons isformation of low and high pressure zones. Typically, cloudsare formed by collection of moisture and this moisture iscondensed due to the coldness of the air. The condensed FIGURE 1.1 – A TYPICAL LOW PRESSURE ZONE IN A NOAA SATELLITEsubstance can constitute of variety of elements such as water IMAGE [14]drops, dust, or smoke particles in the air. It is a naturalattribute of air particles that start condensing when the There are various types of low pressure zones such astemperature gets colder in the air. Hence, when the air rises tropical storms, sub-polar cyclones, extra-tropical cyclones,higher in altitude, the moisture in the air forms the clouds and sub-arctic cyclones [1]. Low pressure zones are often[1]. On the other hand, when the air rises to increase in associated with stronger winds. This activity typicallytemperature, partial vacuum is created that leads to an area of produces heavy cloud cover when the air gets cooler.low pressure in the air. Here, rain can be a result if the air is Typically, low pressure activity brings cloudy and overcastadequately moisturized. skies. Low pressure is also the major reason of temperature In the response of this natural activity, the formation of levels of in both winter and summer due to the momentouslow and high pressure zones in air is the most significant overcast weather.activity [1]. The whole weather system is under the influence A low pressure center is indicated in figure 1.2 by "L"of formation and deformation of these low and high pressure and is formed by an inner area and an outer area. In the innerzones on the various parts of the earth [3]. Identification of area of a low pressure zone, the wind flow in counter clockthese low and high pressure zones in satellite imagery is one wise direction [1]. While in the outer edges of the lowof the significant issues involved in weather forecasting. In pressure zone, the wind flows clockwise around the mainthis research paper, we propose the use of PCA (Principal area of the low pressure.Component Analysis) [2] approach to automate the B. High Pressure Zoneidentification of low and high pressure zones in images. Asingle met-satellite sends about 2 GB of image data in 24 Similar to low pressure zone, a high pressure zone is ahours a day [3]. It is really important to automate this process particular region of air where the level of air pressure is
  • 2. highest with respect to its surroundings. By moving away and high pressure zones. Training is performed using nfrom the center of the high pressure zone in any horizontal images.direction, the pressure will gradually decrease [1]. In other a) Constructing Row Vector: First of all, each inputwords, a high pressure zone may be surrounded by so many image is mapped from a 2-D representation to a row vectorlow pressure zones. In figure 1.2, a high pressure center is [8]. Each row of the images is concatenated with the nextrepresented by “H”. [11] row in a sequence to construct a row vector can be construct a row vector. In Figure 2.1, a m x n matrix is mapped a row vector 1x mn. FIGURE 2.1 - A ROW VECTOR OF A 2-D LOW PRESSURE IMAGE. b) Constructing Row Vector Matrix: In this step, a matrix of the previously generated row vectors is FIGURE 1.2 - LOW AND HIGH PRESSURE ZONES constructed by horizontally concatenating the the row High pressure zone is also referred to as anticyclones. vectors of n cloud images. This row vector matrix representsHighs are frequently associated with light and less intensive the whole input image data. Xi is a row vector of a samplewinds. In these areas, the wind moves in the clockwise image i. where i = 1…. n.direction with a high pressure at the center in the northernhemisphere. High pressure activity typically brings clearskies as such zones are generally major cause of fair weatherand clear skies. Rest of the paper is structured as the section 2 describesthe used PCA algorithm in details and also provides theinformation about the architecture of the designed system. Insection 3, the details about perfumed experiments have beendescribed and retrieved results have also been discussed. Thelast section discusses the conclusion and the possible future FIG 2.2 - WHOLE CLOUD DISTRIBUTION. ROW VECTOR OF XIwork of the conducted research. c) Constructing Mean Cloud Vector: To extract the II. MATERIALS AND METHODS principl features from the whole images sapce, a mean cloud For the detection of the low and high pressure zones, vector Ψ of n row vectors is calculated as follows:clockwise and anticlockwise patterns are detected from the Ψ = (1/n) Σ X i where i = 1 .… n (1)input satellite images [4]. The work presented in this d) Construting Difference Matrix: In this step, a newresearch paper is based on PCA to demonstrate the basicconcept of Eigenvectors [5]. The eigenvectors are typically a matrix Φ is constructed by subtracting mean cloud vector Ψsmall group of features that play a major role in shaping an from each cloud image X of training set.image. Following are the details, how PCA has been used to Φi = Xi - Ψ where i = 1 .… n (2)process satellite images. e) Creating Covariance Matrix: To find eigenvectors, a matrix C (i.e. Data Covariance Matrix) [9] is calculated byA. Used Algorithm taking product of matrix Φ and its transpose matrix Φt. PCA algorithm typically has two phases [7]. The phases Here, t represents transpose of a matrix.are described as following: C = Φt Φ (3) 1. Training Phase 2. Recognition Phase f) Selecting Eigenvectors: The generated eigenvectors in previous step analyzed to select the 20 highest valued 1) Training Phase: This training phase construct an eigenvectors. These selected eigenvectors make an imageimage-space of the input satellite images. An image space is space from the resultant covariance matrix C.used for classification in the testing phase. The sample inputimage data is used in training phase to train the designed The creation of covariance matrix and selection ofsystem. In this training phase, the designed system extracts eigenvectors involve intensive computations. Hence, these steps are performed using Matlab 6.0.the knowledge regarding the possible patterns of the low
  • 3. a) Testing Phase: In testing phase, an image is first 1) Image Acquisition: This module acquires andanalyzed and its principal features are located. Then these preprocesses the input image. The figure 2.4 shows theprincipal features are compared with the principal features of sample images used for training purpose.images-space [6]. If some match found there, then the imageare classified according to the previously defined rules.Recognition or testing phase is performed in the followingtwo steps. b) In this step, the contents of a new satellite image arecategorized by calculating projection Ω on image-space [13]as following: Ω = Ui * ( Z - Ψ ) (4) where, Ui is image-space and Z is the new Image. FIGURE 2.4 – USED TRAINING DATASET OF NOAA IMAGERY c) If the threshold Φ of testing image matches with oneof the thresholds [15] in image space of low pressure zone The images are satellite images. Acquired images arethen the area is marked as low pressure zone and if the converted to 256-bit Gray scale bitmap images. The inputthresholds in the image space of high pressure zones, then images are scaled to 50 x 50 pixel ratio for NOAA-AVHRRthe area is marked as high pressure zones. images. The size ratio of input images can be varied from 25 1 x 25 to 50 x 50. Φi = /k max (Ωi -Ωj) where i,j=1 ….. n (5) 2) Extraction of Attributes: In this module, theB. Designed System workflow particular patterns (i.e. clock-wise and anti-clock-wise patterns) are identified in data of the satellite images. These The designed system for detecting low and high pressure patterns are called the principle features as they representzones in NOAA-AVHRR [16] satellite images for prediction direction of the flow of pixels. Cyclone attributes areof precipitation discriminates the clock-wise cyclones and extracted from images using PCA algorithm.counterclockwise anti-cyclones. This system carries outdetection and prediction in the five modules such as 3) Image Comparison: In image comparison module,acquisition of input image, extraction of the related the principal features of the test image are matched with theattributes, comparison of these attributes, and detection of image-space of already given images in training set. Afterpossible patterns and finally zone identification that can matching this module specifies that rather the satellite imageresult in prediction of precipitation as shown in the following contains the low or high pressure zones are the sky is clear.figure 2.3. 4) Zone Detection: This module practically detects the presence of the low and high pressure zones in the images. Input Acquisition In the given satellite images clockwise and anti-clockwise (Preprocessing input image) patterns are detected. If the zones are present in the satellite image, it is further specified that rather it is low pressure zone (cyclone) or high pressure zone (anti-cyclone). Extraction of Attributes 5) Precipitation Prediction: This is the last module, (Selecting Eigenvectors) which finally predicts about the precipitation. This module tells that what type of precipitation is possible as clear sky, overcast, light rain, heavy rain with thunderstorm, hailing or Image Comparison snow fall on the basis of the size and intensity of the low (Pattern based matching) and high pressure zones detected in the satellite image. III. EXPERIMENTS AND RESULTS A number of experiments were performed to test the Pattern Detection (Anti/clock-wise pattern detection) functionality of the designed system. In this section the details of performed experiments, retrieval of results and then analysis of the results has been presented. Experiments are performed using following steps: Zone Identification (Low & High Pressure Zone Information) 1- Data Gathering 2- Normalizing Satellite Images 3- Detecting Low &High Pressure Zones FIGURE 2.3 – WORKING OF THE DESIGNED SYSTEM 4- Prediction with Data Assimilation 5- Evaluate Accuracy
  • 4. A. Data Gathering PCA handles image details that we can still use an array of The training procedure in any classification system is greater range to get still higher accuracy.quite significant and beneficial. Using various algorithms If the low or high pressure zones are identified in thetraining can be performed. Algorithm used in current image satellite image, the zone are shown depicted by the tool andclassifier system for training is principal component analysis, it the further possible weather prediction are also shown i.e.whose major emphasis is to locate and depict the principal the type of precipitation is possible as clear sky, overcast,features of the given sample image [10]. Sample images light rain, heavy rain with thunderstorm, hailing or snow fallwere retrieved from NOAA HRPT receiving station that was on the basis of the size and intensity of the low and highmaintained at the Institute of Geography [15]. pressure zones detected in the satellite image. This training phase construct an image-space, which isrequired for classification in testing phase. In training phase, D. Prediction with Data Assimilationclassifier system is trained by using sample data input. If it is Weather prediction is performed in this phase with therequired, output pattern can be enhanced and improvised by help of data assimilation process. In this phase, the extractedretraining the system by more refined and conspicuous data. information of the earlier modules is incorporated to attainIn training procedure most significant and consequential the most recent weather forecast [12]. Typically, thefactor is the quality of the training image data set. The numerical models are also used to perform the analysis ofquality of the training images system. meteorological data. The Assimilation module provides the appropriate estimations of the present atmospherically states. The system was tested by 3 types of images as clear sky, low pressure zone images and high pressure zone images. Following is the training time and recognition rate of the system as the number of persons is varied. - Number of satellite image types = 03 - Number of Images per image type = 20 - Training Time = 45 Sec. By increasing number of pressure zone type images theFIGURE 3.1 - CONVERTING ORIGINAL SATELLITE IMAGE INTO 50 X 50 RATIO training time increases but causes recognition to improve. OF 256-BIT GRAY SCALE IMAGE Quality of the training image data set bases on the E. Accuracy Evaluationresolution, light intensity, colour intensity and some other A set of testing images were used to test the accuracy offactors [12]. The training images used in designed software the designed system. There were three types of images suchsystem are colour images and have various coordinates. The as images with clear sky, images with low pressure zonesimages required by designed system should be gray scale 8- and images with high pressure zones. For accuracybit bitmap images and have 50 x 50 pixel ratio. Following evaluation, the processing results of each class of testingare some examples of training images define the ratio of images were compared with other class of images. Forsuccess or failure of the designed system. testing 20 per class were used for the sake of testing such as clear sky, low-pressure and high-pressure image. If a systemB. Normalizing Satellite Images accurately identifies the type of an image its given one point In this phase, the input images are converted to 256-bit otherwise that image is given zero point. The overall score isgray color bitmap image. All image types are converted into shown in table I given below.bitmap image format. Input images are scaled to size 50 x 50 TABLE I. TESTING RESULTS OF DIFFERENT PRESSURE ZONE TYPEpixels. As the greater number of pixels can immensely effect IMAGESthe memory usage so array of smaller range is preferred.This is the complimentary processing requirement of the Clear Low High Images Classsystem. But this ratio can be tuned from 30 x 30 to 50 x 50. sky Pressure PressureThis pre-processing of the input satellite images helps in Clear sky 20 0 0improving the accuracy if the results. Low-Pressure 0 18 2C. Detecting Low & High Pressure Zones High Pressure 1 2 17 The image data set is split into two portions: the firstportion is used for the training of the system and other The accuracy of classification results of low and highportion is used of the testing of the system. PCA algorithm pressure zones is depicted in table II. The accuracy ofus used to identify the clock-wise and anti-click-wise classification of each class is compared with the other classpatterns in the satellite images. Pattern extraction and and in the end, an over all accuracy of the system has alsomatching involves intensive computational processing but been shown in table II.
  • 5. TABLE II. TOTAL ACCURACY = 87.5% intelligent Systems Design and Applications - Volume 02. ISDA. Washington, DC, 51-57. Clear Low High [9] Conde, C., Ruiz, A., and Cabello, E. 2003. PCA vs Low Resolution Image Class sky Pressure Pressure Images in Face Verification. In Proceedings of the 12th international Conference on Image Analysis and Processing (September 17 - 19, Clear sky 100 --- --- 2003). ICIAP. IEEE Computer Society, Washington, DC, 63. Low-Pressure --- 90 10 [10] Yunqi, L., Dongjie, C., Meiling, Y., Qingmin, L., and Zhenxiang, S. 2009. 3D Face Recognition by Surface Classification Image and High Pressure 5 10 85 PCA. Second international Conference on Machine Vision, (ICMV’09) Washington, DC, 145-149. IV. DISCUSSION [11] Baum, B.A, Tovinkere V., Titlow J., Welch R.M., 1997. Automated Cloud Classification of Global AVHRR Data Using a Fuzzy Logic The results have shown that PCA is an effective classifier Approach. Journal of Applied Meteorology.1519-1539.with respect to processing time and accuracy of results. [12] Taejung, K., Im Y. 2003. Automatic Satellite Image Registration byProcessing of satellite images needs high speed processing Combination of Matching and Random Sample Consensus, IEEEwith better accuracy results. PCA has been successfully used Trans. Geoscience and Remote Sensing, 41, 2 (2003)1111-1117to handles complex special information of the satellite [13] Wang, H., Leng, Y., Wang, Z., and Wu, X. 2007. Application ofimages. It has also been observed that PCA is a better image correction and bit-plane fusion in generalized PCA based face recognition. Pattern Recognition Letters. 28, 16 (Dec. 2007), 2352-approach for identification of low and high pressure zones 2358.from the satellite imagery. It can identify the pressure zones [14] Baker W., 2005, NOAA Satellite Images, Institute of Geography,from only those images which have been taken from the University of Copenhagen, Avialble: data set. If the images out of training set are tested (Mar 2010)the accuracy decreases to 10% to 15%. There is still need to [15] Yang, J., Zhang, D., Frangi, A. F., and Yang, J. 2004. Two-improvise the used algorithm to make it more generic and Dimensional PCA: A New Approach to Appearance-Based Facerobust. In this paper only the low and high pressure zones Representation and Recognition. IEEE Trans. Pattern Anal. Mach.have been identified and there are still tropical storms, extra- Intell. 26, 1 (Jan. 2004), 131-137.tropical cyclones, sub-polar cyclones, and sub-arctic [16] Zhang, Z., Zhang, J., Liao, M., and Zhang, L., 2000, Automatic Registration of Multi-Source Imagery Based on Global Imagecyclones to be recognized with higher accuracy in terms of Matching. Photogrammetric Engineering and Remote Sensing, 66,5time and accurate identification and prediction of storms. (2000) 625-629 ACKNOWLEDGMENTS For the experimentation the NOAA satellite images wereused from the webpage of Institute of Geography, Universityof Copenhagen. We are thankful to M. S. Rasmussen forproviding us the possible data products. REFERENCES[1] Simpson B., Burns, 2000. Pressure Patterns: Ecncyclopedia of Atmospheric Environment,. Available: /climate/older/Pressure_Patterns.html (Apr 2010)[2] Bajwa, I. S., Nadim A., Hyder S. I., Naweed M. S., 2009, Feature Based Image Classification by using Principal Component Analysis, Journal of Graphics, Vision and Image Processing, 9(2), 11 - 17.[3] Bankert, R. L., 1994. Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network. Journal of Applied Meteorology, 33, 909–918. Boulder, CO 80307.[4] Khayat, O., Shahdoosti, H. R., and Khosravi, M. H. 2008. Image classification using principal feature analysis. 7th WSEAS international Conference on Artificial intelligence, Knowledge Engineering and Data Bases (Cambridge, UK).[5] Aboalsamh, H. A., Mathkour, H. I., Assassa, G. M., and Mursi, M. F. 2009. Face recognition using principle components and linear discriminant analysis. 8th WSEAS international Conference on Signal Processing, Robotics and Automation (Cambridge, UK).[6] Lee C.A., Kesselman C., Schwab S., 1996, Near-Real- Time Satellite Image Processing: Meta-computing in C++. IEEE Computer Graphics and Applications, 16, 4 (1996), 79-84[7] Bajwa, I. S., Hyder, S. I., 2005. PCA based Image Classification of Single Layered Cloud Types, IEEE Symposium on Emerging Technologies, (Islamabad, Pakistan) 2005, 365-369[8] Kim, K., Oh, A., and Woo, Y. 2008. PCA-Based Face Verification and Passport Code Recognition Using Improved FKCN Algorithm. In Proceedings of the 2008 Eighth international Conference on