scholarly journals DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1269-1273 ◽  
Author(s):  
N. Yagmur ◽  
N. Musaoglu ◽  
G. Taskin
Keyword(s):  
Support Vector Machine ◽  
Shallow Water ◽  
Spatial Resolution ◽  
Water Area ◽  
Water Bodies ◽  
Support Vector ◽  
Spectral Bands ◽  
Shallow Water Area ◽  
Sentinel 2

<p><strong>Abstract.</strong> Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices’ accuracy was obtained as 91.84%.</p>

scholarly journals COMPARISON OF LANDSAT-8 AND SENTINEL-2 DATA FOR CLASSIFICATION OF RABI CROPS OVER KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 579-584
Author(s):  
S. Paul ◽  
D. N. Kumar
Keyword(s):  
Vegetation Index ◽  
Support Vector ◽  
Growth Stages ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Spectral Bands ◽  
Sentinel 2A ◽  
Rabi Crops ◽  
Sentinel 2

<p><strong>Abstract.</strong> Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of <sup>7</sup>C<sub>2</sub> and <sup>9</sup>C<sub>2</sub> NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.</p>

scholarly journals A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 396 ◽  
Author(s):  
Premysl Stych ◽  
Barbora Jerabkova ◽  
Josef Lastovicka ◽  
Martin Riedl ◽  
Daniel Paluba
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bark Beetle ◽  
National Park ◽  
Support Vector ◽  
Landsat 8 ◽  
Kappa Index ◽  
Sentinel 2

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.

scholarly journals Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

2020 ◽  
Vol 12 (4) ◽  
pp. 623 ◽  
Author(s):  
Mutiara Syifa ◽  
Mahdi Panahi ◽  
Chang-Wook Lee
Keyword(s):  
Support Vector Machine ◽  
Hybrid Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Burned Area ◽  
Support Vector ◽  
Landsat 8 ◽  
Svm Algorithm ◽  
Mitigation Planning ◽  
Sentinel 2

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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