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Machine Learning with Remote Sensing Image Data Sets

Biserka Petrovska, Tatjana Atanasova Pacemska, Natasa Stojkovik, Aleksandra Stojanova, Mirjana Kocaleva
2021 Informatica (Ljubljana, Tiskana izd.)  
The proposed transfer learning techniques were validated on two remote sensing image datasets: WHU RS datasets and AID dataset.  ...  In our article, we use transfer learning from pre-trained deep Convolutional Neural Networks (CNN) within remote sensing image classification.  ...  Remote sensing datasets We test our proposed transfer learning techniques on two common aerial scene data sets; the WHU RS data set [43] and the aerial image dataset (AID) [34] .  ... 
doi:10.31449/inf.v45i3.3296 fatcat:xns4uecolrfbjex4aw4dlf23eu

Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework

Xingrui Yu, Xiaomin Wu, Chunbo Luo, Peng Ren
2017 GIScience & Remote Sensing  
remote sensing scene classification, empowered by deep learning.  ...  Compared with the benchmark datasets used in popular deep learning frameworks, however, the volumes of available remote sensing datasets are particularly limited, which have restricted deep learning methods  ...  However, though the amount of remote sensing data keeps increasing every year, the properly labeled remote sensing images available for training a deep machine learning model are still limited.  ... 
doi:10.1080/15481603.2017.1323377 fatcat:fm54suan5nhhthnvi4nu2qebvu

Machine learning in remote sensing data processing

Gustavo Camps-Valls
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
Remote sensing data processing deals with real-life applications with great societal values.  ...  This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.  ...  NEW TRENDS IN MACHINE LEARNING FOR REMOTE SENSING The special characteristics of the acquired data motivates the continuous research in machine learning methods for tackling particular remote sensing problems  ... 
doi:10.1109/mlsp.2009.5306233 fatcat:tb3on4evwvdvpkri67dbph7zfy

Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing

Yue Qu, Xu Qian, Hongqing Song, Yi Xing, Zhengyi Li, Jinqiang Tan
2018 Water  
Combining remote sensing data with experimental data, this paper establishes a machine learning model to reveal the characteristics of SMC.  ...  When comparing different machine learning methods, it can be concluded that the support vector classifier (SVC) method trained with remote sensing and grayscale data can achieve the highest accuracy (76.69%  ...  Results Results of the Three Methods and Two Data Resources In this paper, with 2500 remote sensing data used as the training set and 502 remote sensing data used as the validation set, the machine learning  ... 
doi:10.3390/w10040423 fatcat:z4tzptc3yjgczip56ce3ulj43e

Land Cover Classification using Machine Learning Techniques - A Survey

Vandana Singh, BIT Mesra
2020 International Journal of Engineering Research and  
Enormous amount of spatial data is being produced owing to the availability of cloud base technology such as geographic information, satellite imagery, and analysis remote sensing imagery.  ...  The aim of this research is the review of literature for classification of land cover features using machine learning techniques.  ...  Machine learning techniques help in remote sensing for classification and analysis of remote sensing data to classify the land cover.  ... 
doi:10.17577/ijertv9is060881 fatcat:i4d7xu7cwfgz7nrbyzu6tdr544

Research Progress on Remote Sensing Classification Methods for Farmland Vegetation

Dongliang Fan, Xiaoyun Su, Bo Weng, Tianshu Wang, Feiyun Yang
2021 AgriEngineering  
The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote  ...  Current problems related to crop remote sensing identification are then identified and future development directions are proposed.  ...  Object-Oriented Machine Learning Algorithms With continuous improvements to the spatial resolution of remote sensing images, the image scenes become increasingly complex, and the phenomena of "the same  ... 
doi:10.3390/agriengineering3040061 fatcat:55boz33pybhahihfb23onp7in4

A NOVEL SELF-TAUGHT LEARNING FRAMEWORK USING SPATIAL PYRAMID MATCHING FOR SCENE CLASSIFICATION

Y. Yang, D. Zhu, F. Ren, C. Cheng
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In the experiments, we learn the dictionary on Caltech-101 data set, and classify two remote sensing scene image data sets: UC Merced LandUse data set and Changping data set.  ...  Our work thus provides a new way to reduce resource cost in learning a remote sensing scene image classifier.  ...  ACKNOWLEDGEMENTS We are grateful to Sensing IntelliGence and MAchine learning group, Wuhan University, for their open source remote sensing scene extract software.  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-725-2020 fatcat:ycbptk3difhn7erojz6l3dlosi

Deep Learning Techniques for Geospatial Data Analysis [chapter]

Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam
2020 Learning and Analytics in Intelligent Systems  
(ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques  ...  At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather  ...  Deep learning for Remotely Sensed Data Analytics Images constitute the significant chunk of data acquired through the method of remote sensing.  ... 
doi:10.1007/978-3-030-49724-8_3 fatcat:yv6stldjcjbclbfcx3d6i3s2um

Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries

Aimin Li, Meng Fan, Guangduo Qin, Youcheng Xu, Hailong Wang
2021 Applied Sciences  
machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine  ...  There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms.  ...  Three different areas with different surface features are selected from remote sensing images.  ... 
doi:10.3390/app112110062 fatcat:ewok5olk7fhrja3n4w3eyil2iu

MapReduce-based Parallel Learning for Large-scale Remote Sensing Images

Fenghua Huang
2014 Open Automation and Control Systems Journal  
Machine learning applied to large-scale remote sensing images shows inadequacies in computational capability and storage space.  ...  To solve this problem, we propose a cloud computing-based scheme for learning remote sensing images in a parallel manner: (1) a hull vector-based hybrid parallel support vector machine model (HHB-PSVM)  ...  Machine learning of large-scale remote sensing images is a very complex project which demands a systematic approach.  ... 
doi:10.2174/1874444301406011962 fatcat:rfbclm4qjbbdpbsu2euyshpgky

2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees]

Naoto Yokoya, Pedram Ghamisi, Ronny Haensch, Michael Schmitt
2020 IEEE Geoscience and Remote Sensing Magazine  
His research interests include interdisciplinary research on remote sensing and machine (deep) learning, image and signal processing, and multisensor data fusion.  ...  His research focuses on image analysis and machine learning applied to the extraction of information from remote sensing observations.  ... 
doi:10.1109/mgrs.2020.2970124 fatcat:gw74vyo6erc6fmrxujrjyszbti

Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data [article]

Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-Fan Chen, John Anderson
2021 arXiv   pre-print
We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks.  ...  The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.  ...  The increase of remote sensing data availability, computational resources, and advances in machine learning provide unprecedented opportunities for data-driven approaches to estimate wildfire likelihood  ... 
arXiv:2010.07445v3 fatcat:zu2l57iuwffdti4lnocwevm5qa

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

Jacinta Holloway, Kerrie Mengersen
2018 Remote Sensing  
With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data.  ...  We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature.  ...  Remote Sens. 2018, 10, 1365  ... 
doi:10.3390/rs10091365 fatcat:dnc5d73szjgk3lfn2c2kzsmbay

Feature Extraction and Content Based Image Retrieval for High Resolution Remote Sensing Images

2019 International journal of recent technology and engineering  
CBIR (Content Based Image Retrieval) when used with HRRS (High Resolution Remote Sensing) images will yield with effective data.  ...  These are the days where we are very rich in information and poor in data. This is very true in case of image data.  ...  After the machine is fully trained and equipped with all sort of images the machine behaves well and classify features and extract valuable information from Remote Sensing images. II.  ... 
doi:10.35940/ijrte.c6677.098319 fatcat:pwnopkpmpbek5kcpk7ffaicwui

Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures [chapter]

Mihai Datcu, Gottfried Schwarz, Corneliu Octavian Dumitru
2020 Recent Trends in Artificial Neural Networks - from Training to Prediction  
The corresponding test and validation data sets are an integral part of the learning process and also of the algorithm performance evaluation.  ...  Therefore, we need reliably labeled data sets and tools to train the developed algorithms and to assess the performance of our deep learning paradigms.  ...  Acknowledgements We appreciate the cooperation with Politehnica University of Bucharest (UPB) in Romania and our project partners from the European H2020 projects CANDELA (under grant agreement No. 776193  ... 
doi:10.5772/intechopen.90910 fatcat:ajwyldahcvggnfwax7ecl6wx4u
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