In today's existence, satellite imaging plays an important role in identifying landscape features on the surface of the earth. Sophisticated sensors record the apparent hue and the other spectrums in high-resolution images. Satellite imaging is interpreted and analysed for advanced remote sensing applications including meteorology, oceanography, fisheries, agriculture, conservation of habitats, forestry, landscape, geology, mapping, regional planning, education, intelligence and wars. Digital image processing has become a challenge with the accelerated development of these emerging application domains. The photographs collected by the satellite were interpreted and analysed In order to recognised the exact objects on the earth surface that provide a lot of geographical detail, interpretation and analysis have been performed on the satellite images. Classifying objects on images is actually a major field of study in the processing of pictures. It can thus be used in different applications in real time. Recent classification works on the objects present in satellite images are based on assembly processes, a mixture of one or more classification systems. For image improvement, precise segmentation and classification of input images, new methods are proposed. A new algorithm is applied in order to increase the contrast and denounce the input image resulting in a very high quality output image at the integrated stages of the work "Integrated wavelet-based Satellite Image Enhancement (IWSE)".

Keywords: Image enhancement, Integrated wavelet-based satellite image enhancement, PSNR, MSE, Classifying objects.

[1] Acton, S.T., Molloy, J.A. and Yu, Y., “Three-dimensional Speckle Reducing Anisotropic Diffusion”, IEEE Conference Record of the 37th Asilomar Conference on Signals, Systems and Comp., Vol. 2, pp. 1987-1991, 2003.

[2] Adams, J.B. and A.R. Gillespie., “Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach”, Cambridge University Press, 2006.

[3] Adams R., and L. Bischof, “Seeded region growing,” IEEE Transaction Pattern Analysis Machine Intell., Vol. 16, No. 6, pp. 641-647, 1994.

[4] Afshin Asefpour, Keyvan Asefpour, “A  new  satellite  Image Segmentation enhancement technique for weak image boundaries”, Annals of Faculty Engineering Hunedoara – International Journal of Engineering, Vol. 10, No. 3, pp. 83-86, 2012.

[5] Ajay K. Mandava and Emma Regentova, “Image denoising based on adaptive  nonlinear diffusion wavelet domain”, Journal of Electronic Imaging, Vol. 20, No. 3, 2011.

[6] J. Amoros Lopez, E. Izquierdo Verdiguier, L. Gomez Chova, J. Munoz Mari, J.Z. Rodriguez Barreiro, G. Camps Valls, J. Calpe Maravilla, “Land cover classification of VHR airborne images for citrus grove identification”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 1, pp. 115-123, 2011.

[7] Andreu, F., Ballester, C.,  Caselles,  V. and Mazn, J.M.,  “Minimizing total variation flow, Differential and Integral Equations”, Vol. 14, No. 3, pp. 321-360, 2001.

[8] Antoniadis, A. and Bigot, J., “Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study”, Journal of Statistical Software, Vol. 6, No. 1, pp.1-83, 2001.

[9] Behrooz Ghandeharian, Hadi Sadoghi Yazdi and Faranak Homayouni, “Modified Adaptive Center Eighted Median Filter for Uppressingimpulsive Noise in Images”, International Journal of Research and Reviews in Applied Sciences, Vol. 1, No. 3, pp. 218-227, 2009.

[10] Bettahar S, Stambouli A.B, Lambert P,  Benoit  A,  “PDE-Based  Enhancement of Color Images in RGB Space”, Image Processing, IEEE Transactions, Vol. 21, No. 5, pp. 2500-2512, 2012.

[11] Beucher S, Rivest J.F, Soille P., “Morphological gradients”, Journal of Electron Imaging, Vol. 3, No. 1, Pp.326-336, 1993.

[12] Boncelet, C., Alan C. Bovik, “Image Noise Models”, Handbook of Image and Video Processing, Academic Press, ISBN 0121197921, 2005.

[13] Boonwat Attachoo and Petcharat Pattanasethanon, “A New Approach for Colored Satellite Image Enhancement”, International Conference on Robotics and Biomimetics, pp. 1365-1370, 2009.

[14] Boser, B.E., Guyon, I.M., and Vapnik, V. “A Training Algorithm for Optimum Margin Classifiers”, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh. ACM, 1992.

[15] Bruce,  Donoho,  D.,  YeGeo,  H.,  “Wavelet  Analysis”,  IEEE  Spectrum, pp. 27-35, 1996.

[16] Byun, Young Gi, Han, You Kyung, Chae, Tae Byeong, “A multispectral image segmentation approach for object based image classification of high resolution satellite imagery”, KSCE Journal of Civil Engineering, Springer Science & Business Media, Vol. 17, No. 2, pp. 486-497, 2013.

[17] L. Jubairahmed, S. Satheeskumaran, and C. Venkatesan, “Contourlet transform based adaptive nonlinear diffusion filtering for speckle noise removal in ultrasound images,” Cluster Computing, vol. 22, no. S5, pp. 11237–11246, Nov. 2017.

[18] L. J. Ahmed, “Discrete Shearlet Transform Based Speckle Noise Removal in Ultrasound Images,” National Academy Science Letters, vol. 41, no. 2, pp. 91–95, Apr. 2018.

[19] G. Rajesh Hien Dang, K. Martin Sagayam, S. Dhanasekar, "Image Fusion based on Sparse Sampling Method and Hybrid Discrete Cosine Transformation", International Journal of Scientific and Technology Research, vol.8, no.12, pp.1103-1107, December 2019.

[20] J.Dhanasekar, "Multimodal Biometric System Based on Dorsal and Palm Vein Images", Journal of Xidian University, vol.14, no.07, pp.1498-1503, 2020.

[21] M. Mohankumar, T. Thamaraimanalan, N. Sanjeev, (2017) “Distortion Correction Scheme for Multiresolution Camera Images”, Asian Journal of Applied Science and Technology, Vol 1, Issue 1, pp 195-198.