6,551
Views
17
CrossRef citations to date
0
Altmetric
Review Article

An investigation in satellite images based on image enhancement techniques

ORCID Icon, &
Pages 86-94 | Received 16 Apr 2019, Accepted 24 Sep 2019, Published online: 02 Oct 2019

References

  • Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., & Pippi, I. (2001). Information-theoretic assessment of sampled hyperspectral imagers. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1447–1458. doi:10.1109/36.934076
  • Aiazzi, B., Alparone, L., Baronti, S., & Garzelli, A. (2002). Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40(10), 2300–2312. doi:10.1109/TGRS.2002.803623
  • Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., & Selva, M. (2006). MTF-tailored multiscale fusion of high-resolution MS and Pan imagery. Photogrammetric Engineering & Remote Sensing, 72(5), 591–596. doi:10.14358/PERS.72.5.591
  • Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., & Nencini, F. (2006, July). A new method for MS+ Pan image fusion assessment without reference. In 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 3802–3805). IEEE.
  • Amolins, K., Zhang, Y., & Dare, P. (2007). Wavelet based image fusion techniques—An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 249–263. doi:10.1016/j.isprsjprs.2007.05.009
  • Ashraf, S., Brabyn, L., & Hicks, B.J. (2013). Alternative solutions for determining the spectral band weights for the subtractive resolution merge technique. International Journal of Image and Data Fusion, 4(2), 105–125. doi:10.1080/19479832.2011.607473
  • Atkinson, P.M. (2005). Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogrammetric Engineering & Remote Sensing, 71(7), 839–846. doi:10.14358/PERS.71.7.839
  • Ballester, C., Caselles, V., Igual, L., Verdera, J., & Rougé, B. (2006). A variational model for P+ XS image fusion. International Journal of Computer Vision, 69(1), 43–58. doi:10.1007/s11263-006-6852-x
  • Bayarri, M.J., Berger, J.O., Paulo, R., Sacks, J., Cafeo, J.A., Cavendish, J., … Tu, J. (2007). A framework for validation of computer models. Technometrics, 49(2), 138–154. doi:10.1198/004017007000000092
  • Ben Abbes, A., Bounouh, O., Farah, I.R., de Jong, R., & Martínez, B. (2018). Comparative study of three satellite image time-series decomposition methods for vegetation change detection. European Journal of Remote Sensing, 51(1), 607–615. doi:10.1080/22797254.2018.1465360
  • Brown, M., Gunn, S.R., & Lewis, H.G. (1999). Support vector machines for optimal classification and spectral unmixing. Ecological Modelling, 120(2–3), 167–179. doi:10.1016/S0304-3800(99)00100-3
  • Chen, X., Zou, D., Zhiying Zhou, S., Zhao, Q., & Tan, P. (2013). Image matting with local and nonlocal smooth priors. In 2013 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, China, (pp. 1902–1907).
  • Chibani, Y. (2007). Integration of panchromatic and SAR features into multispectral SPOT images using the ‘a tròus’ wavelet decomposition. International Journal of Remote Sensing, 28(10), 2295–2307. doi:10.1080/01431160600606874
  • Dehnavi, S., & Mohammadzadeh, A. (2013). A new developed GIHS-BT-SFIM fusion method based on edge and class data. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1(3), 139–145. doi:10.5194/isprsarchives-XL-1-W3-139-2013
  • Devika, G., & Parthasarathy, S. (2018). Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image. European Journal of Remote Sensing, 51(1), 754–764. doi:10.1080/22797254.2018.1482731
  • Duran, J., Coll, B., & Sbert, C. (2013). Chambolle’s projection algorithm for total variation denoising. Image Processing on Line, 2013, 311–331. doi:10.5201/ipol.2013.61
  • Garzelli, A., Nencini, F., Alparone, L., & Baronti, S. (2005, July). Multiresolution fusion of multispectral and panchromatic images through the curvelet transform. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005, Netherland. IGARSS’05. (Vol. 4, pp. 2838–2841). IEEE.
  • Gavankar, N.L., & Ghosh, S.K. (2018). Automatic building footprint extraction from high-resolution satellite image using mathematical morphology. European Journal of Remote Sensing, 51(1), 182–193. doi:10.1080/22797254.2017.1416676
  • Gewali, U.B., Monteiro, S.T., & Saber, E. (2018). Machine learning based hyperspectral image analysis: A survey. arXiv Preprint arXiv, 1802.08701.
  • González-Audícana, M., Saleta, J.L., Catalán, R.G., & García, R. (2004). Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1291–1299. doi:10.1109/TGRS.2004.825593
  • Gu, Y., Zhang, Y., & Zhang, J. (2008). Integration of spatial–Spectral information for resolution enhancement in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1347–1358. doi:10.1109/TGRS.2008.917270
  • Günlü, A., Ercanlı, İ., Sönmez, T., & Başkent, E.Z. (2014). Prediction of some stand parameters using pan-sharpened IKONOS satellite image. European Journal of Remote Sensing, 47(1), 329–342. doi:10.5721/EuJRS20144720
  • Guo, M., Ma, H., Bao, Y., & Wang, L. (2018). Fusing panchromatic and swir bands based on cnn-a preliminary study over worldview-3 datasets. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42, 3.
  • Han, X., Yu, J., Luo, J., & Sun, W. (2019). Hyperspectral and multispectral image fusion using cluster-based multi-branch bp neural networks. Remote Sensing, 11(10), 1173.
  • Hashimoto, N., Murakami, Y., Bautista, P.A., Yamaguchi, M., Obi, T., Ohyama, N., … Kosugi, Y. (2011). Multispectral image enhancement for effective visualization. Optics Express, 19(10), 9315–9329. doi:10.1364/OE.19.009315
  • Hubert, M., & Rousseeuw, P. J., & Vanden Branden, K. (2005). ROBPCA: a new approach to robust principal component analysis. Technometrics, 47(1), 64–79
  • Jayanth, J., Kumar, T.A., & Koliwad, S. (2018). Fusion of multispectral and panchromatic data using regionally weighted principal component analysis and wavelet. Current Science, 115(10), 1938. doi:10.18520/cs/v115/i10/1938-1942
  • Kaplan, N.H. (2018). Weighted intensity hue saturation transform for image enhancement and pansharpening. Turkish Journal of Electrical Engineering and Computer Science, 26(1), 204–219. doi:10.3906/elk-1704-43
  • Kotwal, K., & Chaudhuri, S. (2010, December). A fast approach for fusion of hyperspectral images through redundancy elimination. In Proceedings of the seventh Indian conference on computer vision, graphics and Image processing (pp. 506–511). ACM. doi:10.1177/1753193409347495
  • Kotwal, K., & Chaudhuri, S. (2012). An optimization-based approach to fusion of hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 501–509. doi:10.1109/JSTARS.2012.2187274
  • Lal, A.M., & Anouncia, S.M. (2016). Enhanced dictionary based sparse representation fusion for multi-temporal remote sensing images. European Journal of Remote Sensing, 49(1), 317–336. doi:10.5721/EuJRS20164918
  • Leung, Y., Liu, J., & Zhang, J. (2014). An improved adaptive intensity–Hue–Saturation method for the fusion of remote sensing images. IEEE Geoscience and Remote Sensing Letters, 11(5), 985–989. doi:10.1109/LGRS.2013.2284282
  • Levin, A., Rav-Acha, A., & Lischinski, D. (2008). Spectral matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10), 1699–1712. doi:10.1109/TPAMI.2008.168
  • Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2019). An underwater image enhancement benchmark dataset and beyond. arXiv Preprint arXiv, 1901.05495.
  • Mallat, S.G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 11(7), 674–693. doi:10.1109/34.192463
  • Malpica, J.A. (2007). Hue adjustment to IHS pan-sharpened IKONOS imagery for vegetation enhancement. IEEE Geoscience and Remote Sensing Letters, 4(1), 27–31. doi:10.1109/LGRS.2006.883523
  • Maselli, F., Chiesi, M., & Pieri, M. (2016). A novel approach to produce NDVI image series with enhanced spatial properties. European Journal of Remote Sensing, 49(1), 171–184. doi:10.5721/EuJRS20164910
  • Md Noor, S., Ren, J., Marshall, S., & Michael, K. (2017). Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. Sensors, 17(11), 2644. doi:10.3390/s17050968
  • Metwalli, M.R., Nasr, A.H., Faragallah, O.S., El-Rabaie, E.S.M., Abbas, A.M., Alshebeili, S.A., & Abd El-Samie, F.E. (2014). Efficient pan-sharpening of satellite images with the contourlet transform. International Journal of Remote Sensing, 35(5), 1979–2002. doi:10.1080/01431161.2013.873832
  • Miao, Q., & Wang, B. (2006, April). The contourlet transform for image fusion. In Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006 (Vol. 6242, p. 62420Z). International Society for Optics and Photonics, Florida.
  • Mozgovoy, D.K., Hnatushenko, V.V., & Vasyliev, V.V. (2018). Automated recognition of vegetation and water bodies on the territory of megacities in satellite images of visible and ir bands. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4, 3.
  • Nunez, J., Otazu, X., Fors, O., Prades, A., Pala, V., & Arbiol, R. (1999). Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1204–1211. doi:10.1109/36.763274
  • Parveen, R., Kulkarni, S., & Mytri, V.D. (2018). Automated extraction and discrimination of open land areas from IRS-1C LISS III imagery. International Journal of Computers and Applications, 1–10. doi:10.1080/1206212X.2018.1558937
  • Pohl, C., & Van Genderen, J.L. (1998). Review article multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823–854. doi:10.1080/014311698215748
  • Pradhan, P.S., King, R.L., Younan, N.H., & Holcomb, D.W. (2006). Estimation of the number of decomposition levels for a wavelet-based multiresolution multisensor image fusion. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3674–3686. doi:10.1109/TGRS.2006.881758
  • Qu, J., Lei, J., Li, Y., Dong, W., Zeng, Z., & Chen, D. (2018). Structure tensor-based algorithm for hyperspectral and panchromatic images fusion. Remote Sensing, 10(3), 373. doi:10.3390/rs10030373
  • Rahmani, S., Strait, M., Merkurjev, D., Moeller, M., & Wittman, T. (2010). An adaptive IHS pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 7(4), 746–750. doi:10.1109/LGRS.2010.2046715
  • Rajathurai, A., & Chellakkon, H.S. (2018). Improved visualization using a fusion technique based on KNN matting of remotely sensed images. Journal of the Indian Society of Remote Sensing, 46(2), 179–187. doi:10.1007/s12524-017-0693-7
  • Raman, S., & Chaudhuri, S. (2007, October). A matte-less, variational approach to automatic scene compositing. In 2007 IEEE 11th International Conference on Computer Vision (pp. 1–6), Bombay. IEEE.
  • Ruescas, A.B., Sobrino, J.A., Julien, Y., Jiménez-Muñoz, J.C., Sòria, G., Hidalgo, V., … Mattar, C. (2010). Mapping sub-pixel burnt percentage using AVHRR data. Application to the Alcalaten area in Spain. International Journal of Remote Sensing, 31(20), 5315–5330. doi:10.1080/01431160903369592
  • Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., & Lang, S. (2017). Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases. European Journal of Remote Sensing, 50(1), 452–463. doi:10.1080/22797254.2017.1357432
  • Tu, T.M., Huang, P.S., Hung, C.L., & Chang, C.P. (2004). A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 309–312. doi:10.1109/LGRS.2004.834804
  • Villa, A., Chanussot, J., Benediktsson, J.A., & Jutten, C. (2011). Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution. IEEE Journal of Selected Topics in Signal Processing, 5(3), 521–533. doi:10.1109/JSTSP.2010.2096798
  • Vrabel, J. (2000). Multispectral imagery advanced band sharpening study. Photogrammetric Engineering and Remote Sensing, 66(1), 73–80.
  • Wang, H., & Suter, D. (2007). A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recognition, 40(3), 1091–1105. doi:10.1016/j.patcog.2006.05.024
  • Wang, Q., Jia, Z., Qin, X., Yang, J., & Hu, Y. (2011). A new technique for multispectral and panchromatic image fusion. Procedia Engineering, 24, 182–186. doi:10.1016/j.proeng.2011.11.2623
  • Wang, Y.X., & Zhang, Y.J. (2013). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336–1353. doi:10.1109/TKDE.2012.51
  • Wenyan, Z., Zhenhong, J., Yu, Y., Yang, J., & Kasabov, N. (2018). SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain. European Journal of Remote Sensing, 51(1), 785–794. doi:10.1080/22797254.2018.1491804
  • Xiao-Hui, Y.A.N.G., & Li-Cheng, J.I.A.O. (2008). Fusion algorithm for remote sensing images based on nonsubsampled contourlet transform. Acta Automatica Sinica, 34(3), 274–281. doi:10.3724/SP.J.1004.2008.00274
  • Xie, Q., Zhou, M., Zhao, Q., Meng, D., Zuo, W., & Xu, Z. (2019). Multispectral and hyperspectral image fusion by MS/HS fusion net. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1585–1594), China.
  • Xu, N., Price, B., Cohen, S., & Huang, T. (2017). Deep image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2970–2979), China.
  • Xu, Q., Zhang, Y., & Li, B. (2014). Recent advances in pansharpening and key problems in applications. International Journal of Image and Data Fusion, 5(3), 175–195. doi:10.1080/19479832.2014.889227
  • Xu, Q., Zhang, Y., Li, B., & Ding, L. (2015). Pansharpening using regression of classified MS and pan images to reduce color distortion. IEEE Geoscience and Remote Sensing Letters, 12(1), 28–32. doi:10.1109/LGRS.2014.2324817
  • Yadav, P., & Agrawal, S. (2018). Road network identification and extraction in satellite imagery using otsu’s method and connected component analysis. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-5, 91–98. doi:10.5194/isprs-archives-XLII-5-91-2018
  • Yocky, D.A. (1996). Multiresolution wavelet decomposition I me merger of landsat thematic mapper and SPOT panchromatic data. Photogrammetric Engineering & Remote Sensing, 62(9), 1067–1074.
  • Zhang, Y., & Mishra, R.K. (2014). From UNB PanSharp to Fuze Go–The success behind the pan-sharpening algorithm. International Journal of Image and Data Fusion, 5(1), 39–53. doi:10.1080/19479832.2013.848475