Establishing the best spectral bands and timing of imagery for land use – land cover (LULC) class separability using Landsat ETM+ and Terra MODIS data

2007 ◽  
Vol 33 (5) ◽  
pp. 431-444 ◽  
Author(s):  
C M Biradar ◽  
P S Thenkabail ◽  
Md. A Islam ◽  
M. Anputhas ◽  
R Tharme ◽  
...  
Author(s):  
Arindom Ain

Abstract: Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering techniques for generating 5 different class labels for training the model. These labels are further used to develop a predictive model to classify LULC classes. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices


2017 ◽  
Vol 04 (03) ◽  
pp. 272-277
Author(s):  
Tawhida A. Yousif ◽  
Nancy I. Abdalla ◽  
El-Mugheira M. Ibrahim ◽  
Afraa M. E. Adam

2011 ◽  
Vol 13 (5) ◽  
pp. 695-700
Author(s):  
Zhihua TANG ◽  
Xianlong ZHU ◽  
Cheng LI

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