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Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8

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@article{IJASEIT4059,
   author = {K. Sateesh Kumar and G. Sreenivasulu},
   title = {Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {1},
   year = {2018},
   pages = {199--204},
   keywords = {Homographic filter; Landsat8; Un-decimated dual-tree complex wavelet transform; vegetation indices.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4059},
   doi = {10.18517/ijaseit.8.1.4059}
}

EndNote

%A Kumar, K. Sateesh
%A Sreenivasulu, G.
%D 2018
%T Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8
%B 2018
%9 Homographic filter; Landsat8; Un-decimated dual-tree complex wavelet transform; vegetation indices.
%! Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8
%K Homographic filter; Landsat8; Un-decimated dual-tree complex wavelet transform; vegetation indices.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4059
%R doi:10.18517/ijaseit.8.1.4059
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 1
%@ 2088-5334

IEEE

K. Sateesh Kumar 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, pp. 199-204, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.1.4059.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Kumar, K. Sateesh
AU  - Sreenivasulu, G.
PY  - 2018
TI  - Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 1
Y2  - 2018
SP  - 199
EP  - 204
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Homographic filter; Landsat8; Un-decimated dual-tree complex wavelet transform; vegetation indices.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4059
DO  - 10.18517/ijaseit.8.1.4059

RefWorks

RT Journal Article
ID 4059
A1 Kumar, K. Sateesh
A1 Sreenivasulu, G.
T1 Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 1
YR 2018
SP 199
OP 204
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Homographic filter; Landsat8; Un-decimated dual-tree complex wavelet transform; vegetation indices.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4059
DO  - 10.18517/ijaseit.8.1.4059