scholarly journals Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (13) ◽  
pp. 2069 ◽  
Author(s):  
Vanesa Mateo-Pérez ◽  
Marina Corral-Bobadilla ◽  
Francisco Ortega-Fernández ◽  
Eliseo P. Vergara-González
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
European Space Agency ◽  
Weather Conditions ◽  
Practical Method ◽  
Support Vector ◽  
Single Beam ◽  
Testing Dataset ◽  
Sentinel 2 ◽  
Echo Sounder

Knowledge of the free draft of ports is essential for the adequate management of ports. To maintain these drafts, it is necessary to carry out dredging periodically, and to conduct bathymetries using traditional techniques, such as echo sounding. However, an echo sounder is very expensive and its accuracy is subject to weather conditions. Thus, the use of recent advancements in remote sensing techniques provide a better solution for mapping and estimating the evolution of the seabed in these areas. This paper presents a cost-effective and practical method for estimating satellite-derived bathymetry for highly polluted and turbid waters at two different ports in the cities of Luarca and Candás in the Principality of Asturias (Spain). The method involves the use of the support vector machine (SVM) technique and open Sentinel-2 satellite imagery, which the European Space Agency has supplied. Models were compared to the bathymetries that were obtained from the in situ data collected by a single beam echo sounder that the Port Service of the Principality of Asturias provided. The most accurate values of the training and testing dataset in Candás, were R2 = 0.911 and RMSE = 0.3694 m, and R2 = 0.8553 and RMSE = 0.4370 m, respectively. The accuracies of the training and testing dataset values in Luarca were R2 = 0.976 and RMSE = 0.4409 m, and R2 = 0.9731 and RMSE = 0.4640 m, respectively. The regression analysis results of the training and testing dataset were consistent. The approaches that have been developed in this work may be included in the monitoring of future dredging activities in ports, especially where the water is polluted, muddy and highly turbid.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

scholarly journals Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 86 ◽  
Author(s):  
Ping Liu ◽  
Xi Chen
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Support Vector ◽  
Sufficient Information ◽  
Classification Result ◽  
Rotation Forest ◽  
Svm Algorithm ◽  
Multi Temporal ◽  
Occurrence Matrix

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area.

scholarly journals Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

2019 ◽  
Vol 40 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Toyohiro Hamaguchi ◽  
Takeshi Saito ◽  
Makoto Suzuki ◽  
Toshiyuki Ishioka ◽  
Yamato Tomisawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Peak Velocity ◽  
Hand Movement ◽  
Classification Model ◽  
Support Vector ◽  
Leap Motion ◽  
Stroke Patients ◽  
Related Data ◽  
Testing Dataset ◽  
Linear Svm

Abstract Purpose Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists. Methods A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements. Results High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006). Conclusion This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

scholarly journals Alzheimer Disease Diagnosis from fMRI images Based on Latent Low Rank Features and Support Vector Machine (SVM)

2019 ◽  
Vol 14 ◽  
Author(s):  
Nastaran Shahparian ◽  
Mehran Yazdi ◽  
Mohammad Reza Khosravi
Keyword(s):  
Support Vector Machine ◽  
Alzheimer Disease ◽  
Neurological Diseases ◽  
Practical Method ◽  
Disease Diagnosis ◽  
Low Rank ◽  
Gaussian Kernel ◽  
Fmri Data ◽  
Support Vector ◽  
Svm Classifier

Purpose: In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer disease. Design/methodology/approach: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to healthy category, mild stage of the disease or Alzheimer stage. Findings: The obtained classification accuracy for the proposed method is more than 97.5%. Originality/value: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.

scholarly journals Deteksi Permukiman Kumuh Menggunakan Informasi Spektral dan Tekstur Citra Multiresolusi Spasial (studi di sebagian Kota Yogyakarta)

2021 ◽  
Vol 19 (1) ◽  
pp. 35-45
Author(s):  
Achmad Fadhilah ◽  
Prima Widayani ◽  
Iswari Nur Hidayati
Keyword(s):  
Support Vector Machine ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Grey Level ◽  
Sentinel 2

Pemetaan dan identifikasi merupakan tahap awal dalam program peningkatan kualitas permukiman kumuh. Pemetaan permukiman kumuh saat ini masih menggunakan metode survei langsung yang membutuhkan banyak biaya, waktu, dan tenaga. Penelitian ini bertujuan untuk mendeteksi keberadaan permukiman kumuh menggunakan citra satelit multiresolusi spasial sebagai metode alternatif dalam mengidentifikasi permukiman kumuh. Citra yang digunakan dalam penelitian ini antara lain: Pleiades-1B, SPOT-7, dan Sentinel-2. Studi ini berlokasi di sebagian Kota Yogyakarta yang dibagi dua daerah penelitian. Algortima Support Vector Machine (SVM) digunakan untuk mengkelaskan permukiman kumuh dan bukan kumuh. Parameter yang digunakan dalam penelitian ini antara lain: Saluran multispektral, Grey Level Co-occurrence Matrx (GLCM), dan Normalized Difference Vegetation Index (NDVI). Validasi dilakukan dengan menggabungkan data sekunder peta permukiman kumuh dan hasil observasi lapangan. Hasil penelitian menunjukkan bahwa tingkat akurasi klasifikasi tertinggi dihasilkan dari layer Sentinel-2 GLCM 3x3 sebesar 56,26% pada daerah penelitian 1, sedangkan pada daerah penelitian 2 diperoleh dari layer Pleiades-1B GLCM 9x9 sebesar 66,17%.

scholarly journals Sentinel-2 Data for Land Cover/Use Mapping: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2291 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Serajis Salekin ◽  
Vincent R. Nyirenda ◽  
Yuji Murayama ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Positive Impact ◽  
European Space Agency ◽  
Remotely Sensed ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Free Access ◽  
Remotely Sensed Data ◽  
Sentinel 2

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

scholarly journals Detection of Vegetation Encroachment in Power Transmission Line Corridor from Satellite Imagery Using Support Vector Machine: A Features Analysis Approach

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3393
Author(s):  
Fathi Mahdi Elsiddig Haroun ◽  
Siti Noratiqah Mohamed Deros ◽  
Mohd Zafri Bin Baharuddin ◽  
Norashidah Md Din
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Power Transmission ◽  
Statistical Moments ◽  
Support Vector ◽  
Vegetation Density ◽  
Color Spaces ◽  
Coverage Area ◽  
Vegetation Encroachment

Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. These methods are very effective in detecting vegetation encroachment. However, they are expensive with regard to the coverage area. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. This method aims to distinguish between high- and low-density vegetation regions along the power line corridor right-of-way (ROW). The results of the study showed that the statistical moments of the color spaces contribute positively to the classification accuracy while some of the gray level co-occurrence matrix (GLCM) features contribute negatively to the classification accuracy. Therefore, a combination of the most effective features was used to achieve a recall accuracy of 98.272%.

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