The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


A Technique for Burning Area Identification Using IHS Transformation and Image Segmentation

#
  In this paper, we have designed and developed a tec hnique for burning area identification using Intensity Hue Saturation (IHS) transformation and image segmentat ion. The process of identifying the burnt area in proposed technique consists of four steps such as: IHS transformation, object segmentation, identification of smoke area using Feed-Forward Neural Network (FFNN) and discovering burning areas from the smoke segments. Here, satellite image collected from NASA is utilized for the experimental study of the propo sed research. The images obtained from the NASA is given to HIS transformation that convert the RGB image into inte nsity, hue, saturation transformed image so that, this process is suitable for segmentation process. After the transformation of image, object segmentation technique is done bas ed on K-means clustering algorithm. Subsequently, FFNN is used fo r identification of smoke area from the segments. After identifying the smoke segment, the burning area is identified throu gh directional analysis. The proposed burnt area id entification technique is analyzed with the help of sensitivity, specificity and the accuracy. Finally, experimental results say that, the proposed technique is achieved the overall accuracy 2.6%, wh ich is better than the existing approach.   


[1] Anggraeni A. and Lin C., Application of SAM and SVM Techniques to Burned Area Detection for Land Sat TM Images in Forests of South Sumatra, in Proceedings of the 2 nd International Conference on Environmental Science and Technology , Singapore, pp. 160-164, 2011.

[2] Awad M., An Unsupervised Artificial Neural Network Method for Satellite Image Segmentation, the International Arab Journal of Information Technology , vol. 7, no. 2, pp. 199- 205, 2010.

[3] Carla R., Santurri L., Bonora L., and Conese C., Multitemporal Burnt Area Detection Methods based on a Couple of Images Acquired After the Fire Event, in Proceedings of the 5 th International Wildland Fire Conference , Sun City, South Africa, 2011.

[4] Carper W., Lillesand T., and Kieffer R., The Use of Intensity-Hue-Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data, Photogrammetric Engineering and Remote Sensing , vol. 56, no. 4, pp. 459-467, 1990.

[5] Chang C., Hyperspectral Imaging: Techniques for Spectral Detection and Classification , Kluwer Academic Publishers, 2001.

[6] Giglio L., Loboda T., Roy D., Quayle B., and Justice C., An Active-Fire based Burned Area Mapping Algorithm for the MODIS Sensor, Remote Sensing of Environment , pp. 408-420, 2009.

[7] Haralick R., Shanmugam K., and Dinstein I., Texture Features for Image Classification, IEEE Transactions on Systems , Man and Cybernetics , vol. 8, no. 6, pp. 610-621, 1973.

[8] Holden Z., Evans J., Using Fuzzy C-means and Local Autocorrelation to Cluster Satellite- Inferred Burn Severity Classes, International Journal of Wildland Fire , vol. 19, no. 7, pp. 853- 860, 2010.

[9] Jasinski M., Estimation of Subpixel Vegetation Density of Natural Regions using Satellite Multispectral Imagery, IEEE Transactions on Geoscience and Remote Sensing , vol. 34, no. 3, pp. 804-813, 1996.

[10] Landgrebe D., Signal Theory Methods in Multispectral Remote Sensing , John Wiley and Sons, 2003.

[11] Lu D. and Weng Q., A Survey of Image Classification Methods and Techniques For Improving Classification Performance, International Journal of Remote Sensing , vol. 28, no. 5, pp. 823-870, 2007.

[12] Mahi H., Isabaten H., and Serief C., Zernike Moments and SVM for Shape Classification in Very High Resolution Satellite Images, vol. 11, no. 1, pp. 43-51, 2014.

[13] Mitri G. and Gitas I., A Performance Evaluation of a Burned Area Object-Based Classification Model When Applied to Topographically and Non-Topographically Corrected TM Imagery, INT. J. Remote Sensing , vol. 25, no. 14, pp. 2863-2870, 2004.

[14] Nasa Fire Image Gallery, available at: http://www.nasa.gov/mission_pages/fires/main/u sa/index.html, last visited 2013.

[15] Shine J. and Carr D., A Comparison of Classification Methods for Large Imagery Data Sets, JSM 2002 Statistics in an ERA of Technological Change-Statistical Computing section , New York, USA, pp. 3205-3207, 2002.

[16] Sifakis N., Iossifidis C., Kontoes C., and Keramitsoglou I., Wildfire Detection and Tracking over Greece using MSG-SEVIRI Satellite Data, Remote Sensing, vol. 3, no. 3, pp. 524-538, 2011.

[17] Stroppiana D., Bordogna G., Carrara P., Boschetti M., Boschetti L., and Brivio P., A Method For Extracting Burned Areas From Landsat TM/ETM+ Images by Soft Aggregation of Multiple Spectral Indices and a Region Growing Algorithm, ISPRS Journal of Photogrammetry and Remote Sensing , vol. 69, pp. 88-102, 2012.

[18] Tansey K., Chambers I., Anstee A., Denniss A., and Lamb A., Object-Oriented Classification of Very High Resolution Airborne Imagery for the Extraction of Hedgerows and Field Margin Cover in Agricultural Areas, Applied Geography, vol. 29, no. 2, pp. 145-157, 2009.

[19] Tso B. and Mather P., Classification Methods for Remotely Sensed Data , Taylor and Francis, 2001.

[20] Tuominen S. and Pekkarinen A., Performance of Different Spectral and Textural Aerial A Technique for Burning Area Identification Using IHS Transformation and Image Segmentation 771 Photograph Features in Multi-Source Forest Inventory, Remote Sensing of Environment , vol. 94, no. 2, pp. 256-268, 2005.

[21] Varshney P. and Arora M., Advanced Image Processing Techniques for Remote Sensed Hyperspectral Data , Springer-Verlag, 2004.

[22] Wang L., Qu J., and Hao X., Forest Fire Detection using the Normalized Multi-Band Drought Index (NMDI) with Satellite Measurements, Agricultural and Forest Meteorology , vol. 148, no. 11, pp. 1767-1776, 2008.

[23] Wang S., Miao L., and Peng G., An Improved Algorithm for Forest Fire Detection using HJ Data, in Proceedings of the 18 th Biennial Conference of International Society for Ecological Modelling , Procedia Environmental Sciences , Beijing, China, pp. 140-150, 2012.

[24] White J., Ryan K., Key C., and Running S., Remote Sensing of Forest Fire Severity and Vegetation Recovery, International Journal of Wildland Fire , vol. 6, no. 3, pp. 125-136, 1996. Thumma Kumar obtained his Bachelor s degree in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, India in 2005. Then he obtained his Master s degree in Geo Informatics and Surveying Technology in the same university in 2007 and currently pursuing PhD in Spatial Information Technology from Jawaharlal Nehru Technological University, India. Currently, he is working as a Consultant Application Developer in Computer Sciences Corporation at Hyderabad, India. His specializations include working with different kind s of images, image classifications, image processing too ls and applications developed in MATLAB. His current research interests are Identification of Forest bur nt areas using Intensity, Hue and Saturation technique s. Kamireddy Reddy received his B.Tech. (Civil Engineering) (1983- 1987) from Sri Venkateswara University, Tirupati and M.Tech. (Remote Sensing) (1989-1991) and PhD (Remote Sensing and GIS) (2009) from Andhra University, Visakhapatnam. He joined in National Remote Sensing Centre (NRSC), Department of Space, Govt. of India, Hyderabad during 1992 as Scientist/Engineer SC . H e worked as Technical Secretary to the Director, NRSC and played a key role in Programme, Planning and Evaluation Group (PPEG) for 11 years. He also, headed Disaster Watch Team and worked as a Team member in Flood Disaster Management, Remote Sensing Application Group, NRSC for 2 years. Subsequently, he joined as the Director, (2004-2007 ) A.P. State Remote Sensing Applications Centre (APSRAC), Planning Department, Govt. of A.P. on deputation basis and elevated as the Director Gener al (2008-2011 for 4 years). He also held an additional responsibility of Director (Tech.), (2006-2011) at Andhra Pradesh State Disaster Mitigation Society (APSDMS), Planning Department, Govt. of Andhra Pradesh. At present Dr.K.M Reddy is working as Scientist SG , at NRSC in Remote Sensing Applications Area (RSA) looking after part of SIS-D P activities since November 2011. He is a member of several apex scientific bodies in the country. He h as more than 50 publications to his credit published i n various journals of national and international repu te.