2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)最新文献_第3页

GeoAI for Disaster Mitigation: Fire Severity Prediction Models using Sentinel-2 and ANN Regression GeoAI用于减灾:基于Sentinel-2和ANN回归的火灾严重程度预测模型

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993515

Syamani D. Ali, I. Ridwan, M. Septiana, A. Fithria, Arfa Agustina Rezekiah, Adi Rahmadi, Mufidah Asyari, Hidayatul Rahman, Gita Ayu Syafarina

{"title":"GeoAI for Disaster Mitigation: Fire Severity Prediction Models using Sentinel-2 and ANN Regression","authors":"Syamani D. Ali, I. Ridwan, M. Septiana, A. Fithria, Arfa Agustina Rezekiah, Adi Rahmadi, Mufidah Asyari, Hidayatul Rahman, Gita Ayu Syafarina","doi":"10.1109/ICARES56907.2022.9993515","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9993515","url":null,"abstract":"Wildfire is a common disaster that hits Indonesia every dry season, especially on the islands of Kalimantan and Sumatra. In order to reduce the impact of fire hazards, preventive measures are needed before the occurrence of fires. One of them is by setting up an information system such as EWS. The aim of this study is to create an effective image- and machine learning-based predictive model of the severity of forest and land fires based on vegetation conditions prior to burning. Three parameters of prefire vegetation conditions, namely vegetation greenness indices, vegetation moisture, and vegetation senescence, were selected as independent variables to predict the postfire dependent variable, i.e., fire severity. There are 25 vegetation greenness index options tested, using either ANN regression or multiple linear regression. The vegetation moisture information is represented by the Normalized Difference Moisture Index (NDMI). The vegetation senescence information is extracted using the Plant Senescence Reflectance Index (PSRI). Meanwhile, the wildfire severity is measured using the Burned Area Index for Sentinel-2 (BAIS2). All vegetation conditions and wildfire severity information were extracted from Sentinel-2 imageries. The topology of ANN regression models is configured from one to six hidden layers. More than 100,000 pixels are used as samples, which are then separated into training samples and validation samples. The results of model development and testing show that ANN regression with Inverted Red-Edge Chlorophyll Index (IRECI) as a vegetation greenness parameter is the model that has the highest accuracy in predicting wildfire severity.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep learning for Ground Penetration Radar Reflection Images in Civil Structures Investigation 土木结构勘察中探地雷达反射图像的深度学习

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993511

G. P. Dinanta, D. Fernando, N. Setyaningrum, F. Meliani, J. Widodo, A. Setiyoko, R. Arief

{"title":"Deep learning for Ground Penetration Radar Reflection Images in Civil Structures Investigation","authors":"G. P. Dinanta, D. Fernando, N. Setyaningrum, F. Meliani, J. Widodo, A. Setiyoko, R. Arief","doi":"10.1109/ICARES56907.2022.9993511","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9993511","url":null,"abstract":"Ground Penetrating Radar (GPR) is one of the NDT (Non-Destructive Techniques) in the geophysics field. Most Geoscientists accept the instrument's capability to conduct near-surface mapping. On the other side, the technology in deep learning vastly developed in many sectors, affecting the perspective of radar-images post-processing. The problem occurred when a lot of identical objects were detected in the GPR section. Hence, the interpreter will face difficulties when performing manual object detection on a large scale of the dataset. In this study, the deep learning algorithm attempted to be employed to forage the civil structures and deal with overtired work interpretations. This study specifies five structures from the dataset: Pile, Pipe, Powerline, Rebar, and Void/Collapse Structure. All objects are confirmed buried in the subsurface when field measurement is conducted. This study introduces a new approach to improving accuracy called IC-CNN (Integrated Contouring in Convolutional Neural Network). The IC-CNN method is expected to become an advanced technique to achieve solid identifications for GPR data through an object contour and object localization. The B-Scan of GPR Images was employed for the analysis. However, the primary processing of GPR data has been conducted to make it adequate as relevant input. As a result, it presented a correlation with a 95% confidence level. Furthermore, IC-CNN gave significance $pm 3.5$ % rather than CNN for the GPR B-scan data, which was concluded after 2,500 iterations. In final, the IC-CNN is promising as long as it is well-processed.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115479771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Serial Bus as Communication Between Microcontroller and AD9824 for Multi Spectral Camera 串行总线作为多光谱相机单片机与AD9824之间的通信

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993468

Gafur Hasan Zam Bahari, A. Hadi Syafrudin, Khairunnisa

引用次数: 0

Genetic Algorithms Optimization of a Reinforcement Learning-based Controller for Vertical Landing Rocket Case 基于遗传算法的火箭垂直降落案例强化学习控制器优化

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9992304

Diva Kartika Larasati, Larasmoyo Nugroho, S. Wijaya, R. Andiarti, Rini Akmeliawati, P. Prajitno, Ery Fitrianingsih

引用次数: 0

Relationships of Class Number Variation and Image Classification Accuracy in the LAPAN-A3 Multispectral Imager LAPAN-A3多光谱成像仪类数变化与图像分类精度的关系

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9992294

E. A. Anggari, P. R. Hakim, A. Herawan, S. Salaswati, W. Hasbi

{"title":"Relationships of Class Number Variation and Image Classification Accuracy in the LAPAN-A3 Multispectral Imager","authors":"E. A. Anggari, P. R. Hakim, A. Herawan, S. Salaswati, W. Hasbi","doi":"10.1109/ICARES56907.2022.9992294","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9992294","url":null,"abstract":"LAPAN-A3 has a multispectral imager payload that can be used for earth observation. One of its uses is for land use and land cover classification. To find out the suitability of the classification results with the actual data, it is necessary to calculate accuracy. This study aims to find out the relationship between variations in the number of classes and the accuracy of the classification results of LAPAN-A3 compare with Landsat-8. The research was conducted in 4 study areas in Indonesia, namely Mandailing Natal Regency (North Sumatra), Pandeglang Regency (Banten), Semarang City (Central Java), and Kupang Regency (NTT). It can be concluded that the accuracy is very good in the classification of 2 classes where the accuracy value is more than 95%. Good accuracy in the classification of 4 classes with an accuracy value of more than 85%. The accuracy is good enough in the 6 class classification with an accuracy value of 80%. The blur effect is the reason of the decrease in accuracy due to the less optimal ability to separate spectrals.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130674071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Measurement of SDR based AIS Transmitter 基于SDR的AIS发射机性能测量

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993510

Maulana Ali Arifin, W. Hasbi, Narender Kumar, Nova Maras Nurul Khamsah, Eriko Nasser Nasemudin

{"title":"Performance Measurement of SDR based AIS Transmitter","authors":"Maulana Ali Arifin, W. Hasbi, Narender Kumar, Nova Maras Nurul Khamsah, Eriko Nasser Nasemudin","doi":"10.1109/ICARES56907.2022.9993510","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9993510","url":null,"abstract":"As the largest maritime country, Indonesia needs a maritime monitoring system. It encourages the Indonesian Research Center for Satellite Technology to develop a satellite carrying a satellite-based Automatic Identification System (AIS) receiver. Following the successes of the previous satellite, the currently developed satellite, which is scheduled to be launched in the next few years, will also carry a satellite-based AIS receiver. In developing a satellite-based AIS receiver, an AIS transmitter with adequate flexibility is needed to carry out various testing. This paper aims to investigate the performance of the SDR-based AIS Transmitter. Two kinds of SDR for comparison are LimeSDR Mini and HackRF One. The output RF power, operating frequency, occupied bandwidth, and harmonic signals were measured. The result shows that the output RF power is around 7 dBm for HackRF One and 14 dBm for LimeSDR Mini with direct transmission. However, with the amplifier implemented, the total RF output power for both SDRs is around 33 dBm ±0.2 dBm, which complies with the carrier power error standard. The frequency operation also meets the requirement with less than 150 Hz drift. Likewise, the occupied bandwidth matches the requirement with less than 11 kHz. Finally, the harmonics for LimeSDR Mini and HackRF One would meet the standard, with the suggestion in implementing additional filter.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129013539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Random Forest Algorithm on Sentinel-2A Imagery for Garlic Land Classification Based on Growing Phase in Sembalun 基于Sentinel-2A图像的随机森林算法在Sembalun大蒜生长阶段土地分类中的应用

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993563

Khairunnisa, Annisa, I. S. Sitanggang

引用次数: 1

Aerosol Index and Machine Learning for Fire Smoke Mapping using The Second-generation Global Imager (SGLI) Data over Tropical Peatland Environments 基于第二代全球成像仪(SGLI)数据的气溶胶指数和机器学习在热带泥炭地环境中进行火灾烟雾测绘

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993492

G. A. Chulafak, A. I. Pambudi, P. Sofan

{"title":"Aerosol Index and Machine Learning for Fire Smoke Mapping using The Second-generation Global Imager (SGLI) Data over Tropical Peatland Environments","authors":"G. A. Chulafak, A. I. Pambudi, P. Sofan","doi":"10.1109/ICARES56907.2022.9993492","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9993492","url":null,"abstract":"In this study, we explored the second-generation global imager (SGLI) of Global Change Observation Mission-Climate (GCOM-C) data to map biomass fires over tropical peatlands in Indonesia. The Absorbing Aerosol Index (AAI) derived from the near-Ultraviolet spectrum of SGLI at 250 m spatial resolution was examined statistically to perform smoke and other aerosol sources mapping. The mean values of AAI were statistically different among smoke, cloud, and other aerosols; however, the histogram distribution of AAI over those objects suggested a mixture of AAI regions between smoke and cloud. Machine learning algorithms overcame this limitation. Random Forest (RF) algorithm performs better than the Support Vector Machine (SVM) in mapping smoke from the cloud and other aerosol sources using all bands of SGLI, including the nonpolarization bands, polarization bands, and AAI image. RF performs 87% overall accuracy in classifying four objects, i.e., smoke, cloud, other aerosols, and free-aerosol background objects. The RF accuracy increased to 97% in mapping two classes, i.e., smoke and non-smoke, with the error of commission and omission at 4% and 3%, respectively. This finding provides a high potential for using SGLI data by RF algorithm for smoke detection over tropical peatland regions. More training samples of smoke in various conditions can enrich the artificial intelligence smoke database, which can be adapted as the input for developing the RF modeling using other hyperspectral sensors.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124237825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of CART Algorithm and Cropping Calendar in Estimating Paddy Growth Stage in Karawang Regency, West Java CART算法与种植历法在西爪哇卡拉旺县水稻生育期估算中的比较

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/ICARES56907.2022.9993473

Nadira Fawziyya Masnur, Nurul Izza Afkharinah, Elisabeth Gunawan, A. Agustan, S. Yulianto, K. Mutijarsa, Abdul Karim

{"title":"Comparison of CART Algorithm and Cropping Calendar in Estimating Paddy Growth Stage in Karawang Regency, West Java","authors":"Nadira Fawziyya Masnur, Nurul Izza Afkharinah, Elisabeth Gunawan, A. Agustan, S. Yulianto, K. Mutijarsa, Abdul Karim","doi":"10.1109/ICARES56907.2022.9993473","DOIUrl":"https://doi.org/10.1109/ICARES56907.2022.9993473","url":null,"abstract":"Classification And Regression Trees (CART) is one of the classic and simple algorithm in predictive modeling machine learning. This study aims to compare the result of paddy growth stage estimates based on CART model of Sentinel-1A Synthetic Aperture Radar (SAR) data and Cropping Calendar (KATAM). The construction of the CART model utilises real data field from Area Frame Sampling (Kerangka Sampling Area or KSA) in Karawang Regency observed on 2020. The CART algorithm makes predictions using a tree structure or hierarchical structure. The CART algorithm focuses on finding a decision tree model that has a Gini impurities value = 0. The rules for classifying class based on the physical polarization spectrum which is represented by pixel digital number from Vertical-Vertical (VV), Vertical-Horizontal (VH), and VV/VH of SAR image properties. This study found that the initial planting time is different. The CART model estimates the initial planting time is on September, while the KATAM estimates on November-December.","PeriodicalId":252801,"journal":{"name":"2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124199794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Front Cover Page 封面

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI: 10.1109/icares56907.2022.9993538

引用次数: 0