2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies
Rainfall Estimation Over Roof-top Using Land-Cover Classification of Google Earth Images Medha Aher1
Sadashiv Pradhan2
Yogesh Dandawate3
1, Student, Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune 411048 (INDIA)
[email protected]
2, Student, Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune 411048 (INDIA)
[email protected]
3, Professor, Senior Member IEEE, Dept. of Electronics and Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune 411048 (INDIA)
[email protected]
In India, about 74 per cent of the rainfall is received during the south-west monsoon period of June to September. Due to this high concentration of rainfall over a few months and geographically highly uneven distribution of rainfall there is a poor utilization of rain-water [4][5]. The water balance is a budgeting exercise that assesses the proportion of rainfall that becomes stream flow (or runoff), evapotranspiration, and drainage (or groundwater recharge). In India, The normal annual rainfall precipitation is estimated to be 400 million hectare-meters (Mha-m) of water. Out of this, 115 Mha-m enters surface flows, 215 Mha-m enters the ground, and 70 Mha-m is lost to evaporation. Finally only 25 Mha-m is used through surface irrigation which constitutes merely 6 per cent of the total water available through rain [4]. In Urban areas, the roof top rainwater has been conserved and used for recharge of groundwater. The main focus of this proposed work is to classify land cover andland use area. Land cover is the physical material such as grass, asphalt, trees, bare ground, water, etc. at the surface of the earth. Land cover classification is a fundamental parameter describing the Earth’s surface. Land use is a description of how people utilize the landscape whether for development, conservation, or mixed uses. A land cover map with sufficient calibrationcan be used to identify spatial patterns of physical quantities such as vegetation cover as well as more abstract phenomena such as land use. Accurate information on land cover is required for both scientific research (e.g., climate change modeling, flood prediction) and management (e.g., city planning, disaster mitigation).Satellite images consist of various images of the same object taken at different wavelengths in the visible, infrared or thermal range.Information related to size, shapes, colors, texture etc can be easily extracted using satellite images as it contains much more information than a standard RGB image. Such images have been used for urban land cover classification [6] [7], urban planning [8], soil test, to study forest dynamics [9]. In this paper we are proposing an efficient technique for classifying the satellite images into land cover wherein rooftop area estimation is done accurately for water harvesting purpose. The proposed classification technique includes four
Abstract-‘Water’ is one of the most valuable resources available to the mankind. In the world, due to exponential growth in population and industrialization we are witnessing scarcity of water. In addition, water table levels are falling rapidly than ever. Hence proper management and appropriate utilization of water has become the need of an hour. Hence this problem is required to be tackled with the novel approach. The idea behind this proposal is to design and development of rain water harvesting system based on rainfall runoff estimation over rooftop. The GoogleEarth image is combination of remote sensed satellite images and aerial photographs. The information on land use and land cover is obtained using satellites Google Earth images which are simple, economical and precise approach. In the proposed work an efficient classification technique is proposed in which K-means clustering algorithm and textural parameters based on GLCM are used for classification of the Google Earth images into land cover and land use sector. In Land use and land cover classification whole image gets classified into different region such as Grass area, Water area, Roof-top area, Soil area etc. Then area under the different regions is computed. Area measurement is required for computing rainfall runoff using estimation model. Experimental result shows that the computation of the areas of roof tops and road surfaces are nearly accurate and rainfall runoff calculation can be estimated very near to actual. Index term: Satellite images, Land use and Land cover classification, Image Segmentation, K-means, Color image segmentation classification, Texture analysis, Water harvesting.
I. INTRODUCTION In the world, almost one-fifth of world’s population (about 1.2 billion) lives in area where water is scare [1].According to United Nation (UN) report, in last century water use rate has been increased at more than twice of population increase [2].In India, situation is also same. The total districts in India are 627. In recent years, out of 627 districts, 400 districts that mean 64% of total districts experienced the problem of prolonged period of scanty rainfall [3].In country like India where an amount of precipitation is limited to specific period (monsoon season) and if not utilized, water becomes a scary resource. 978-1-4799-2102-7/14 $31.00 © 2014 IEEE DOI 10.1109/ICESC.2014.24
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phases which are pre-processing, segmentation, classification and analysis. In pre-processing stage, initially input image is passed through the Weiner filter to remove blur from original input image and then using RGB to LAB color conversion method filtered image gets transformed into suitable form for segmentation. For segmentation of image into number of clusters K-means hierarchical clustering algorithm is used. Then Erosion algorithm which is the fundamental operation in morphological image processing is used for shadow removing.Results of segmentation are classified based on texture features. To analyze the texture features that is different objects such as soil, water, rooftop area etc. GLCM (Gray Level Co-occurrence Matrix) parameters are used. Area under the each region is computed, then based on the relationships between the surface type, area and the rainfall, the water runoff is computed. This estimation can be used to design rainwater harvesting schemes for water percolation and recycle/reuse. The rest of the paper is organized as follows: Section II describes the proposed scheme. The method of segmentation of image based on color with K-means clustering and feature extraction based on GLCM parameters is presented and discussed in section III. Section IV describes the data resources and software used. Experimental results obtained using proposed methods are presented in section V. Finally, the performance along with the application is concluded in section VI.
Block Diagram Description • Satellite Image: Use Google Earth image as an input data • Pre-processing: Satellite or Google Earth images cannot be directly segmented. The input satellite image is subjected to a set of pre-processing steps so that the image gets transformed suitably for the further processing. Here the input image gets filtered using Weiner filter to remove blur from original input image and then filtered image converted into LAB color space from RGB color space using RGB to LAB color conversion method as it enable you to quantify visual differences. • Segmentation: The pre-processed image is made of thousands of pixels and it is hectic and time consuming task to classify the whole image based on each of this individual pixel. Also results in increase of error rate and the degraded performance of the classifier system. Hence, we cluster the pre-processed image into clusters [10]. Clustering algorithms used for unsupervised classification of remote sensing data vary according to the efficiency with which clustering takes place. This result in reducing the number of the inputs to the classifier system which reduce the classifier complexity and also the time incurred. It also results in making the system more efficient and accurate. • Shadow Removing: Erosion is one of the fundamental operations in morphological image processing. It was originally defined for binary images later being extended to gray scale images and subsequently to complete lattices. Erosion is mainly used to shrink the object. In our proposed work, erosion algorithm is used for shadow removingas used by Naser Jawas and Nanik Suciati [11]. Steps for shadow removing: Step 1: Convert image into binary with threshold level of 0.5. Step 2: Erode the image with linear structuring element of size 1 by 3. Step 3: Again erode the image with linear structuring element of size 1 by 3. Step 4: Make all the pixels value zero of gray image those are having one value in eroded image. This removes shadow. • Feature Extraction: When the input data to an algorithm is too large to be processed and it is suspected to be redundant then the input data will be transformed into a reduced representation set of features (also known as features vector). It is the first stage of image texture analysis and it transforms input datainto the set of features. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input. The grey-level co-occurrence matrix (GLCM) is the most popular textural feature used to describe spatial properties. • Classification: Classification of remotely sensed data is used to assign corresponding levels with respect to groups with homogeneous characteristics, with the aim of discriminating multiple objects from each other within the image. In this proposed technique, GLCM parameters are used for classification purpose.
II. PROPOSED SCHEME In the proposed scheme, K-means hierarchical clustering is used for segmentation of the image. Then segmented objects are classified based on GLCM parameters. The proposed scheme is shown using block diagram in Figure 1. Satellite Image
Preprocessing
Segmentation
Shadow removing
Feature Extraction/ Texture Analysis
Classification
Texture analysis of different objects
Estimation of water flow from different objects based on different surface models FIG.1.BLOCK DIAGRAM OF PROPOSED SCHEME
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III. K-MEANS CLUSTERING ALGORITHM AND FEATURE EXTRACTION
K-means algorithm considers that each object has its own location in space. Algorithm puts every data point in a cluster such that; that point would be close to every other point in his cluster and as far as possible from every other point from other clusters [16] [17]. Euclidean distance metric is being used in the K-means algorithm. For each data point its Euclidean distance from original cluster is calculated. If that distance is low which means data point is closest to cluster then leave it in that cluster only. If that distance is not closest then move that data point to closest cluster. This process is repeated until and unless process arrives at a point where no data point is being moved from one cluster to other cluster. At this point clustering ends. As K-means approach is iterative, it is computationally intensive and can be treated as unsupervised training areas [18].
A) K-MEANS CLUSTERING ALGORITHM There are many methods of clustering developed for a wide variety of purposes. K-means algorithm has lot of advantages over other algorithms. For instance, K-mean improves accuracy of classification which is drawback of algorithms such as region growing algorithm [12] [13] and nearest neighbor algorithm. In addition to that it can be performed on any type of satellite images including Google Earth images which are free of cost; which eliminates disadvantage of normalized difference index (NDVI) algorithm of being expensive. There is no manual intervention as in case of region growing algorithm (requires seed point allocation); which makes it easier and faster to implement.Hence for clustering, we are using a K-means unsupervised hierarchical clustering algorithm for segmentation of satellite images for land use and land cover classification [14]. For this algorithm L*a*b* color space has been used. The basic aim is to segment colors in an automated fashion using the L*a*b* color space and K-means clustering [15]. The L*a*b* color space is derived from the CIE XYZ tristimulus values. The L*a*b* color space (also known as CIELAB or CIE L*a*b*) enables us to quantify visual differences. The L*a*b* space consists of a luminosity layer 'L*', chromaticitylayer 'a*' indicating where color falls along the red -green axis, and chromaticity-layer 'b*' indicating where the color falls along the blue-yellow axis. Since the color information exists in the 'a*b*' space, our objects are pixels with 'a*' and 'b*' values. Use K means to cluster the objects into required number of K clusters using the Euclidean distance metric. Figure 2 explain the K-means algorithm.
B) FEATURE EXTRACTION Feature extraction is the first stage of image texture analysis. Texture and local spatial statistics can improve classification accuracy in urban areas. Texture is defined as the spatial variation in gray value. It is independent of color or luminance. Texture gives the information about the spatial distribution of gray levels in a neighborhood. The grey-level co-occurrence matrix (GLCM) is the most popular textural feature used to describe spatial properties. The GLCM can fully take into account both the spectral and the spatial distribution of image grey values [19][20][21][22]. In this experiment, the GLCM was calculated over segmented image objects. In GLCM, the number of occurrences of the pair of gray levels i and j which are at a distance d apart in original image corresponds to each entry I (i, j). Four statistical features are used to estimate the similarity between different gray level co-occurrence matrices. The four most relevant features [22] that are widely used are: 1. Contrast: It measures the intensity difference between a pixel and its neighbor over the whole image. For a constant image, contrast has zero value. 2. Correlation: It measures the similarity between a pixel and its neighbor over the whole image. 3. Energy: It is also called as Angular Second Moment. It measures the textural uniformity of an image. 4. Homogeneity: It is inversely proportional to GLCM contrast and also known as Inverse difference moment.
Start
Number of clusters k
Centroid
Distance objects to Centroid
No object move group?
End
C) AREA CALCULATION Grouping based on minimum distance
After classification of an image, area under each cluster is calculated. For that purpose, total area has been taken from Google image using ‘scale’ option. Then number of pixels in each cluster has been calculated to calculate individual cluster area. Using total number of pixels in an image and pixels present in each clustered image, percentage of each cluster in
FIG.2.K-MEANS ALGORITHM
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The segmentation of pre-processed image is carried out using the hierarchical k-means clustering algorithm. Then texture analysis of the segmented objects is carried out using GLCM. Figure 3 shows the original satellite image taken as an input for experimentation and Figure 4 shows the result of Weiner filtering. Figure 5-9 shows the result of k-means segmentation.
an image has been computed. These percentages along with the total area under image have been used to calculate area under each cluster. D) CALCULATION OF WATER RUNOFF Water runoff is calculated by:
ܳ ൌ
ൈൈ ଷ
… (1)
Where, Q = Peak flow in Cumec. i = Rainfall intensity in cm/hr. A = Catchment area in hectares. K = runoff coefficient. [24] K for different cases: sandy soil (0.1-0.2), clayey soil (0.9), loamy soil (0.4), road (0.8), building roof top (0.5), Gravel paving (0.15-0.70). Let us assume i= 0.1 cm/hr That is when 0.1 cm/hr rain will fall on study area; water runoff of Q cubic meters per second will take place.
Fig.5.OUTPUT IMAGEFig.6.SHADOW REMOVED
IV. DATA RESOURCES AND SOFTWARE In this section, we discuss the results of the proposed technique. Study area used is the Google Earth satellite image of Vishwakarma Institute of Information Technology, Pune city, Maharashtra, India with 50 m scale factor which is to be classified as land-cover. The entire work is carried out using MATLAB v7.8.0, Image Processing Toolbox and MS-Office. While taking image from Google Earth we have taken some parameter of satellite image. Here, although specific image has been used to test the results of proposed scheme; this algorithm works on any kind of satellite images subject to change in quality of an image.
Fig.7.ROOF TOP AREAFig.8.ROAD AREA
V. EXPERIMENTAL RESULTS In our proposed classification technique, initially preprocessing is done in which the input image gets transformed suitably for segmentation. In pre-processing, RGB color image gets convert into Lab color space image.
Fig. 9.GREEN AREA AFTER SHADOW REMOVED
Table 1 shows the result of area and water flow calculation by keeping number of clusters is equal to three. Table 2 indicates the parameters of GLCM for each region
Fig.3.ORIGINAL IMAGE Fig.4.FILTERED IMAGE
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maps”;International Geoscience and Remote Sensing Symposium(IGARSS), pp. 201-204, 2011. [7] Wen-ting Cai, Yong-xue Liu, Man-chun Li, Yu Zhang, Zhen Li, “A best-first multivariate decision tree method used for urban land cover classification”, proceedings of 8th IEEE conference on geometrics 2010, Beijing. [8] HolgerThunig, Nils Wolf, Simone Naumann, Alexander Siegmund, Carsten Jurgens, CihanUysal, DeryaMaktav, “Land use land cover classification for applied urban planning - The Challenge of Automation”; Joint Urban Remote Sensing Event JURSE, Munich, Germany, 2011, pp. 229-232. [9] V. Naydenova, G. Jelev, “Forest dynamics study using aerial photos and satellite images with very high spatial resolution”; proceedings of the 4th international conference on recent advance on space technologies “space in the service of society”-RAST 2009,Istanbul,Turkey, Published by IEEE ISBN 978-1-4244-3624-6, 2009, pp. 344-348. [10]Lingkui Meng, Jun Fang, Shurong Lou, Wen Zhang, “Study on image segmentbased land use classification and mapping”, proceeding of International Conference on Information Engineering and Computer Science, ICIECS, 2009, pp: 1-4. [11] Naser Jawas and Nanik Suciati, “Image inpainting using erosion and dilation Operation”, International Journal of Advanced Science and Technology, Vol. 51, February, 2013, pp: 127-134. [12]Gang Wu, Chunhong Pan, Veronique Prinet, and Songde Ma,“Aland use classification methodbasedonregion andedgeinformation fusion”,proceeding of International Conference on Image Processing (ICIP) 2004, vol.3, pp: 17291722. [13] H.S.Lim, M. Z. MatJafri, K. Abdullah, C. J. Wong and N. M. Saleh,“Regional land use/cover classification in Malaysia based onconventional digital camera imageries”, proceeding of IEEE, Aerospace Conference, 2009, pp: 1-7. [14] Ahmed Rekik, Mourad Zribi, Mohammed Benjelloun and Ahmed Ben Hamida, “A K-Means clustering algorithm initialization for unsupervised statistical satellite image segmentation”, proceeding of First International Conference on E-Learning Industrial Electronics, 2006,pp: 11-16. [15] Anil Z. Chitade and Dr. S.K. Katiyar, “Color based image segmentation using K-means clustering”, proceeding International Journal of Engineering Science and Technology, vol.2, No.10, 2010, pp: 5319-5325. [16] Jose L. Marroquin, Federico Girosi,”Some extensions of the K-means algorithm for image segmentation and pattern classification”, Technical Report, MITArtificial Intelligence Laboratory, 1993. [17] M. Luo, Yu-Fei Ma, Hong-Jiang Zhang,”Aspecial constrained K-means approach to image segmentation”, proceeding Joint Conference of Fourth International Conference on InformationCommunications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia,Vol.2, 2003,pp.738-742. [18] Chinki Chandhok and M.Tech Scholar, “Color image segmentation using K-means clustering”; International Journal of VLSI & Signal Processing Applications, Vol.2, Issue 3,June 2012 pp. 241-245. [19] JiaLonghao, Zhou Zhongfa and Li Bo, “Study of SAR image texture feature extraction based on GLCM in Guizhou Karst mountainous region”, proceedings of second International
Table 1. AREA AND WATER FLOW CALCULATION Object
Area(No. of pixels)
Area in square meter
Area in hectare
Roof-top Road Grass
66196 113011 142425
2.1370e+004 3.6483e+004 4.5979e+004
2.1370 3.6483 4.5979
Waterfall flow rate in Cumec 0.0396 0.3818 0.5973
. Table 2.RESULTS OF GLCM PARAMETERS (TEXTURE ANALYSIS) Object Roof-top Road Grass
GLCM parameter’s Contrast
Correlation
Energy
Homogeneity
1.2181 2.1231 1.1754
0.8473 0.6826 0.6731
0.7029 0.5124 0.4292
0.9473 0.8926 0.8776
CONCLUSION From the experimentations it has been noticed that the Kmeans algorithm works very well for the classification of satellite images due to its excellent accuracy. The computation of the roof top and road surfaces is nearly accurate and runoff calculation can be estimated very near to accuracy which in turn is used for design and development of rain water harvesting system. If this water can be stored in tanks it can percolate and the water table level can be increased. The water stored in the purest form can also be treated and used for drinking and purposes. From Google images this experimentation will help to improve the water scarcity problem without much efforts of the survey. ACKNOWLEDGEMENT I am an author, would like to express my immense gratitude towards Prof. Dr.H.B.Dhonde and Prof. Mrs. Ulka Joshi (Dept. of Civil Engineering, Vishwakarma Institute of Information Technology) for their encouragement, supervision and guidance to work on this problem. REFERENCES [1] Report form World Health Organization (WHO) http://www.who.int/features/factfiles/water/en/index.html [2] Statistics: Graphs and Maps – Water use by UN water organization http://www.unwater.org/statistics_use.html [3] Sadashiv Pradhan, Dhiraj Patil, A.V.Chitre and Y.H. Dandawate. “Rainfall water runoff determination using land cover classification of satellite images for rain water harvesting application”, International Journal of Scientific Engineering and Technology, vol.2, No.5, May 2013, pp: 404-410. [4] Vasant P. Gandhi and Vaibhav Bhamoriya, “Rainwater harvesting for irrigation in India”; India Infrastructure Report, 2011, pp. 118-133. [5] Meteorological department of India, Pune, cited in Fertilizer Association of India (2007). [6] Jie Zhang and John Kerekes, “Unsupervised urbane land cover classification using Worldview-2 data and self-organizing
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