Fusion of Airborne Hyperspectral and WorldView2 Multispectral Images for Detailed Urban Land Cover Classification A case Study of Kuala Lumpur, Malaysia

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Detecting the features of urban areas in detail requires very high spatial and spectral resolution in images. Hyperspectral sensors usually offer high spectral resolution images with a low spatial resolution. By contrast, multispectral sensors produce high spatial resolution images with a poor spectral resolution. Therefore, numerous fusion algorithms and techniques have been proposed in recent years to obtain high-quality images with improved spatial and spectral resolutions by sensibly combining the data acquired for the same scene. This work aims to exploit the extracted information from images in an effective way. To achieve this objective, a new algorithm based on transformation was developed. This algorithm primarily depends on the Gram–Schmidt process for fusing images, removing distortions, and improving the appearance of images. Images are first fused by using the Gram–Schmidt pansharpening method. The obtained fused image is utilized in the classification process in different areas by using support vector machine (SVM). The classification result is evaluated using a matrix of errors. The overall accuracy produced from the hyperspectral, multispectral and fused images was 72.33%, 82.83%, and 89.34%, respectively. Results showed that the developed algorithm improved the image enhancement and image fusion. Moreover, the developed algorithm has the ability to produce an imaging product with high spatial resolution and high-quality spectral data.

     


  • Keywords


    Fusion, Hyperspectral, Multispectral, Remote sensing, SVM, Gram-Schmidt, Pan Sharpening

  • References


      [1] Ajaj, Q.M., Pradhan, B., Noori, A.M. and Jebur, M.N., 2017. Spatial Monitoring of Desertification Extent in Western Iraq using Landsat Images and GIS. Land Degradation & Development, 28(8):2418-2431.

      [2] Bendoumi, M.A., He, M., Mei, S. and Zhang, Y., 2012, December. Unmixing approach for hyperspectral data resolution enhancement using high resolution multispectral image. In Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on:1369-1373. IEEE.

      [3] Chen, B., Huang, B. and Xu, B., 2017. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 124, :27-39.

      [4] Chakravortty, S. and Subramaniam, P., 2014. Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), p.1099.

      [5] DigitalGlobe. 2010. Digital Globe Core Imagery Products Guide. Accessed October 20, 2013.http://www.digitalglobe.com/downloads/DigitalGlobe_Core_Imagery_Products_Guide.pdf.

      [6] Eismann, M.T. and Hardie, R.C., 2005. Hyperspe -ctral resolution enhancement using high-resol -ution multispectral imagery with arbitrary response functions. IEEE Transactions on Geoscience and Remote Sensing, 43(3):455-465.

      [7] Gibril, M.B.A., Bakar, S.A., Yao, K., Idrees, M.O. and Pradhan, B., 2017. Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto international, 32(7):735-748.

      [8] Hamedianfar, A., Shafri, H.Z.M., Mansor, S. and Ahmad, N., 2014a. Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images. Journal of Applied Remote Sensing, 8(1), p.085091.

      [9] Hamedianfar, A. and Shafri, H.Z., 2014b. Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery. Geocarto International, 29(3):268-292.

      [10] Hamedianfar, A., Shafri, H.Z.M., Mansor, S. and Ahmad, N., 2014c. Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. Internation

      [11] Hamedianfar, A. and Shafri, H.Z.M., 2015. Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images. International Journal of Remote Sensing, 36(13):3380-3396.

      [12] He, G., Xing, S., Xia, Z., Dong, D. and Wei, Y., 2016, December. Study on WorldView-2 Image Fusion Method Based on NMF and HCS Transform. In Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on :548-553. IEEE.

      [13] Hasanlou, M. and Saradjian, M.R., 2016. Quality assessment of pan-sharpening methods in high-resolution satellite images using radiometric and geometric index. Arabian Journal of Geosciences, 9(1), p.45.

      [14] Khandelwal, A. and Rajan, K.S., 2011, July. Hyperspectral image enhancement based on sensor simulation and vector decomposition. In Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on (pp. 1-6). IEEE.

      [15] Licciardi, G.A., Khan, M.M., Chanussot, J., Montanvert, A., Condat, L. and Jutten, C., 2012. Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction. EURASIP Journal on Advances in Signal processing, 2012(1), p.207.

      [16] Li, H., Jing, L. and Tang, Y., 2017. Assessment of pansharpening methods applied to worldview-2 imagery fusion. Sensors, 17(1), p.89.

      [17] Maurer, T., 2013. How to pan-sharpen images using the Gram-Schmidt pan-sharpen method-a recipe. International archives of the photogrammetry, remote sensing and spatial information sciences, 1, p.W1.

      [18] Mookambiga, A. and Gomathi, V., 2016. Compreh -ensive review on fusion techniques for spatial information enhancement in hyperspectral imagery. Multidimensional Systems and Signal Processing, 27(4):863-889.

      [19] Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S. and Weng, Q., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imag -ery. Remote sensing of environment , 115(5):1145-1161.

      [20] Shareef, M.A., Toumi, A. and Khenchaf, A., 2014. Estimation of water quality parameters using the regression model with fuzzy k-means clustering. international journal of advanced computer science and applications, 5, p.xx.

      [21] Taherzadeh, E. and Shafri, H.Z., 2011, April. Using hyperspectral remote sensing data in urban mapping over Kuala Lumpur. In Urban Remote Sensing Event (JURSE), 2011 Joint : 405-408. IEEE.

      [22] Woycheese, J.P., Pagni, P.J. and Liepmann, D., 1998. Brand lofting above large-scale fires. Building and Fire Research Laboratory, National Institute of Standards and Technology.

      [23] Restaino, R., Vivone, G., Dalla Mura, M. and Chanussot, J., 2016. Fusion of multispectral and panchromatic images based on morphological operators. IEEE Transactions on Image Processing, 25(6):2882-2895.

      [24] Zhou, W. and Troy, A., 2008. An object‐oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11):3119-3135.


 

View

Download

Article ID: 24102
 
DOI: 10.14419/ijet.v7i4.37.24102




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.