Open Access Open Access  Restricted Access Subscription Access

Review on Various Image Processing Techniques in Satellite Imagery Applications

Ganesh C L, Shilpa S, Ananya D, Dr Tabitha Janumala

Abstract


The classification of historical maps has become a crucial task in today's rapidly changing landscape. Changes to city and state boundaries, vegetation areas, water bodies and more can be monitored through satellite images. Therefore, a thorough understanding of satellite image processing is essential for the classification of historical maps. This paper evaluates the advantages and disadvantages of various satellite image processing methods. While many computational methods exist, they perform differently for different applications and choosing the wrong method can lead to subpar results. The paper highlights the appropriate methods for various satellite image processing applications, comparing them to provide insight into the best solution for each problem. This research will assist in the selection of effective techniques for satellite image processing applications.


Full Text:

PDF

References


Ping, X.; Bingqiang, C.; Lingyun, X.; Jingcheng, Z.; Lei, Z.; Hangbo, D. A new MNF– BM4D denoising algorithm based on guided filtering for hyperspectral images. ISA Trans. 2019, 92, 315–324.

Chang, Y.C. A flexible contrast enhancement method with visual effects and brightness preservation: Histogram planting. Comput. Electr. Eng. 2018, 69, 796–807

Suresh, S.; Lal, S. Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl. Soft Comput. J. 2017, 61, 622–641

Singh, H.; Kumar, A.; Balyan, L.K.; Singh, G.K. A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput. Electr. Eng. 2019, 75, 245–261

Tang, S.; Wu, B.; Zhu, Q. Combined adjustment of multi-resolution satellite imagery for improved geo-positioning accuracy. ISPRS J. Photogramm. Remote Sens. 2016, 114, 125– 136.

Vijayaraj, V.; Bright, E.A.; Bhaduri, B.L. Rapid damage assessment from high resolution imagery. Int. Geosci. Remote Sens. Symp. 2008, 3, 1445–1448.

Yuan, X.; Chen, S.; Yuan, W.; Cai, Y. Poor textural image tie point matching via graph theory. ISPRS J. Photogramm. Remote Sens. 2017, 129, 21–31.

Sedaghat, A.; Mohammadi, N. Uniform competency-based local feature extraction for remote sensing images. ISPRS J. Photogramm. Remote Sens. 2018, 135, 142–157.

Rathore, M.M.U.; Ahmad, A.; Paul, A.; Wu, J. Real-time continuous feature extraction in large size satellite images. J. Syst. Archit. 2016, 64, 122–132.

Zhang, L.; Sun, Q. Saliency detection and region of interest extraction based on multi- image common saliency analysis in satellite images. Neurocomputing 2018, 283, 150– 165.


Refbacks

  • There are currently no refbacks.