DOI : https://doi.org/10.18517/ijaseit.8.1.4059

Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8

K. Sateesh Kumar (1), G. Sreenivasulu (2)
(1) Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, S V University, Tirupati, AndhraPradesh-517502, India
(2) Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, S V University, Tirupati, AndhraPradesh-517502, India
Fulltext View | Download
How to cite (IJASEIT) :
Kumar, K. Sateesh, and G. Sreenivasulu. “Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 1, Feb. 2018, pp. 199-04, doi:10.18517/ijaseit.8.1.4059.
Citation Format :
This paper proposed the enhancement of Landsat8 imagery through an Un-decimated Dual-Tree Complex Wavelet Transform (UDT-CWT) based denoising method and modified homographic filter for edge preservation. This work has been extended by estimating several vegetation parameters like Normalized Difference of Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MASVI), and Soil & Atmospherically Resistant Vegetation Index (SARVI). Once the estimation of these parameters was done, the effect of noise was verified. Wavelet decomposes the image into frequency subbands and de-noises each subband separately. These subbands help to increase the resolution. The general problem of the homomorphic filter is that it doesn’t enhance the Low-frequency components which also play a key role in estimating Vegetation Indices (VI).So it was modified to enhance the high-frequency components as well as low-frequency details. Monitoring of vegetation parameters using remote sensing is one of the prominent ways in the estimation of crop yield, Land Use Land Cover (LULC), Water resource management, Drought management, etc. The high-resolution image is more preferable than moderate resolution image to retrieve VI. Image denoising and enhancing the spatial resolution helps to retrieve the parameters well and accurate. The proposed algorithm was working on the images of Landsat8.

H. Demirel, S. Izadpanahi and G. Anbarjafari, 2009,Improved motion-based localized super resolution technique using discrete wavelet transform for low resolution video enhancement, 17th European Signal Processing Conference (EUSIPCO-2009), Edinburgh, Scotland, pp. 1097-1101.

P. Rasti, I. Lusi, H. Demirel, R. Kiefer, and G. Anbarjafari,2014, Wavelet transform based new interpolation technique for satellite image resolution enhancement, IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, pp. 185 -188.

Y. Piao, l. Shin, and H. W. Park, “Image resolution enhancement using inter-subband correlation in wavelet domain”, IEEE International Conference on Image Processing, 2007, Vol. 1, pp. I - 445 - I - 448.

H.Tasmaz, H.Demirel, and G.Anbarjafari,2012, Satellite image enhancement by using dual-tree complex wavelet transform: Denoising and illumination enhancement,20th IEEE Signal Processing and Communications Applications Conferences(SIU2012),pp.1-4.

Pejman Rasti1, Haci TaÅŸmaz et. al., 2016, SATELLITE IMAGE ENHANCEMENT: SYSTEMATIC APPROACH FOR DENOISING AND RESOLUTION, Vol. 91, Issue 3.

D. L. Donoho, 1993, Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data, in Proceedings of Symposia in Applied Mathematics. Vol 47.

N.G.Kingsbury,1998, The dual-tree complex wave transforms a new technique for shift variance and directional filters, IEEE Digital Signal Processing Workshop,pp.319-322.

A. Green, M. Berman, P. Switzer, and M. Craig, 1988, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote sensing,26,1, pp. 65-74.

G. Chang, B. Yu, and M. Vetterli,2000, Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing 9:(9), pp. 1532-1546.

Wenli Liu, Peng He, Hui Li, Hongbo Yu,2012, Improvement on the Algorithm of Homomorphic Filtering, International Conference on Electrical and Computer Engineering, Vol. 11, pp. 120 - 124.

I Wayan Nuarsa, F Nishio and Chiharu Hongo,” Rice Yield Estimation Using Landsat ETM+ Data and Field Observation”, Journal of Agricultural Science, Vol. 4, No. 3; pp 45-56, 2012.

M. Akbari , A. r. Mamanpoush , A. Gieske , M. Miranzadeh , M. Torabi & H. R. Salemi,2006, Crop and land cover classification in Iran using Landsat 7 imagery”, International Journal of Remote Sensing, Vol. 27, No. 19, pp-4117-4135

Kun Jia, Shunlin Liang, Xiangqin Wei, Yunjun Yao, Yingru Su, Bo Jiang and Xiaoxia Wang, 2014, Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data, Remote sensing, 6, pp-11518-11532.

Thomas, V. Benning, and N. Ching, 1987, Classification of remotely sensed images, Adam Hilger, Bristol.

Weicheng Wu,” The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization”, Remote Sens. 2014, 6, pp-1211-1233.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).