scholarly journals MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

2016 ◽  
Vol XLI-B8 ◽  
pp. 1111-1113 ◽  
Author(s):  
H. Ben-Romdhane ◽  
P. R. Marpu ◽  
H. Ghedira ◽  
T. B. M. J. Ouarda
Keyword(s):  
Remote Sensing ◽  
United Arab Emirates ◽  
Benthic Communities ◽  
Arabian Gulf ◽  
Cost Effective ◽  
Support Vector ◽  
Reef Environment ◽  
Conservation Actions ◽  
Spatial Method ◽  
Multispectral Remote Sensing

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

scholarly journals MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

2016 ◽  
Vol XLI-B8 ◽  
pp. 1111-1113
Author(s):  
H. Ben-Romdhane ◽  
P. R. Marpu ◽  
H. Ghedira ◽  
T. B. M. J. Ouarda
Keyword(s):  
Remote Sensing ◽  
United Arab Emirates ◽  
Benthic Communities ◽  
Arabian Gulf ◽  
Cost Effective ◽  
Support Vector ◽  
Reef Environment ◽  
Conservation Actions ◽  
Spatial Method ◽  
Multispectral Remote Sensing

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

2020 ◽  
Vol 123 (4) ◽  
pp. 573-586
Author(s):  
M. Twala ◽  
R. J. Roberts ◽  
C. Munghemezulu
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Bushveld Complex ◽  
Support Vector ◽  
Classification Algorithms ◽  
Error Matrix ◽  
Geological Processes ◽  
Multispectral Remote Sensing ◽  
Eastern Limb

Abstract Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.

scholarly journals Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

2018 ◽  
Vol 10 (7) ◽  
pp. 1119 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Mohammad Rezaee ◽  
Fariba Mohammadimanesh ◽  
Yun Zhang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Support Vector Machine ◽  
State Of The Art ◽  
Support Vector ◽  
Learning Tools ◽  
Deep Convolutional Neural Networks ◽  
Multispectral Remote Sensing

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

scholarly journals Detecting Land use Changes Affected by Human Activities using Remote Sensing (Case Study: Karkheh River Basin)

2015 ◽  
Vol 10 (2) ◽  
pp. 473-481 ◽  
Author(s):  
Saeid Maddah ◽  
Saeed Karimi ◽  
Hadi Rezai ◽  
Jabbar Khaledi
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Cost Effective ◽  
Well Being ◽  
Likelihood Method ◽  
Support Vector ◽  
Running Water ◽  
Land Water

Population growth and abundant activities in order to achieve maximum well-being has forced human to make a lot of changes in the nature. These changes will be cost-effective when they have the minimum damage on the landscape. One of the activities that human did for obtaining the water and preventing flood was making the dam in the track of running water. Since the dam is established until its impoundment and after impoundment, the condition of ecosystem and the appearance of the upstream and downstream of the dam will undergo changes. In this study, using satellite data and remote sensing, these changes have been studied and the landuse changes in vegetation, arid land, water level and residential and non-residential lands is measured in 1998 and 2014 using Maximum Likelihood method and support vector machine.

scholarly journals RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN <i>IN-FILED</i> MAIZE CROP USING UAV REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 467-471 ◽  
Author(s):  
J. Goswami ◽  
V. Sharma ◽  
B. U. Chaudhury ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Abiotic Stress ◽  
Computational Cost ◽  
Cost Effective ◽  
Rapid Identification ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Maize Crop ◽  
Multispectral Data

<p><strong>Abstract.</strong> Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on <i>in-filed</i> Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA&amp;thinsp;=&amp;thinsp;86.47%) with Kappa Indexanalysis (KIA&amp;thinsp;=&amp;thinsp;0.80) and found very cost effective in context of computational cost (time complexity&amp;thinsp;=&amp;thinsp;0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.</p>

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