Machine learning-based ship detection and tracking using satellite images for maritime surveillance

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

Download Full-text

Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

Remote Sensing ◽

10.3390/rs11161907 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1907 ◽

Cited By ~ 3

Author(s):

Mohammad Mardani ◽

Hossein Mardani ◽

Lorenzo De Simone ◽

Samuel Varas ◽

Naoki Kita ◽

...

Keyword(s):

Machine Learning ◽

Developing Countries ◽

Open Access ◽

Land Cover ◽

Satellite Images ◽

National Level ◽

Google Earth ◽

Support Vector ◽

Land Cover Mapping ◽

Sustainable Food

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.

Download Full-text

A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements

Journal of Hydroinformatics ◽

10.2166/hydro.2013.234 ◽

2013 ◽

Vol 15 (4) ◽

pp. 1408-1424 ◽

Cited By ~ 10

Author(s):

Z. Vojinovic ◽

Y. A. Abebe ◽

R. Ranasinghe ◽

A. Vacher ◽

P. Martens ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Shallow Water ◽

Satellite Images ◽

Image Data ◽

Support Vector ◽

Machine Learning Technique ◽

High Resolution Satellite Images ◽

Svm Model ◽

Learning Technique

There has been a rapid growth in the field of remote sensing and its various applications in the area of water management. Nowadays, there are several remote sensing techniques that can be used as a source to derive bathymetry data along coastal areas. The key techniques are: sonar (sound navigating and ranging), LiDAR (light detection and ranging) and high-resolution satellite images. The present paper describes a method which was developed and used to create a shallow water bathymetry data along the Dutch side of Sint Maarten Island by combining sonar measurements and satellite images in a nonlinear machine learning technique. The purpose of this work is to develop a bathymetry dataset that can be used to set up physically-based models for coastal flood modelling work. The nonlinear machine learning technique used in the work is a support vector machine (SVM) model. The sonar data were used as an output whereas image data were used as an input into the SVM model. The results were analysed for three depth ranges and the findings are promising. It remains to further verify the capacity of the new method on a dataset with higher resolution satellite imagery.

Download Full-text

Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques

Remote Sensing ◽

10.3390/rs12091375 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1375

Author(s):

Md Mizanur Rahman ◽

Xunhe Zhang ◽

Imran Ahmed ◽

Zaheer Iqbal ◽

Mojtaba Zeraatpisheh ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

C:N Ratio ◽

Google Earth ◽

Partial Least Square ◽

Landsat Tm ◽

Support Vector ◽

Landsat 8 ◽

Senescent Leaf ◽

Stochastic Gradient Boosting

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Download Full-text

Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images

Remote Sensing ◽

10.3390/rs10111799 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1799 ◽

Cited By ~ 13

Author(s):

Jeong-In Hwang ◽

Hyung-Sup Jung

Keyword(s):

Machine Learning ◽

Satellite Images ◽

Median Filter ◽

Machine Learning Techniques ◽

Support Vector ◽

Sar Images ◽

Ship Detection ◽

Difference Map ◽

Learning Techniques ◽

X Band

In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input neurons and (ii) selecting the correct training data. We focused on generating two optimal input data neurons that (i) strengthened ship targets and (ii) mitigated noise effects by image processing techniques, including median filtering, multi-looking, etc. The median filter and multi-look operations were used to reduce the background noise, and the median filter operation was also used to remove ships in an image in order to maximize the difference between the pixel values of ships and the sea. Through the root-mean-square difference calculation, most ship targets, even including small ships, were emphasized in the images. We tested the performance of the proposed method using X-band high-resolution SAR images including COSMO-SkyMed, KOMPSAT-5, and TerraSAR-X images. An intensity difference map and a texture difference map were extracted from the X-band SAR single-look complex (SLC) images, and then, the maps were used as input neurons for the ANN and SVM machine learning techniques. Finally, we created ship-probability maps through the machine learning techniques. To validate the ANN and SVM results, optimal threshold values were obtained by using the statistical approach and then used to identify ships from the ship-probability maps. Consequently, the level of recall achieved was greater than 90% in most cases. This means that the proposed method enables the detection of most ship targets from X-band SAR images with a reduced number of false detections from negative effects.

Download Full-text

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Download Full-text

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Download Full-text

Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

Remote Sensing ◽

10.3390/rs13081433 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1433

Author(s):

Shobitha Shetty ◽

Prasun Kumar Gupta ◽

Mariana Belgiu ◽

S. K. Srivastav

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Random Sampling ◽

Sampling Design ◽

Remote Sensing Data ◽

Google Earth ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Multi Temporal ◽

Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

Download Full-text

Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

Remote Sensing ◽

10.3390/rs13040787 ◽

2021 ◽

Vol 13 (4) ◽

pp. 787

Author(s):

Lei Zhou ◽

Ting Luo ◽

Mingyi Du ◽

Qiang Chen ◽

Yang Liu ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Google Earth ◽

Construction And Demolition Waste ◽

Parameterization Scheme ◽

Classification Methods ◽

Demolition Waste ◽

Optimal Method ◽

Identification Method ◽

Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

Download Full-text

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

10.5194/egusphere-egu21-1972 ◽

2021 ◽

Author(s):

Xiaotong Zhu ◽

Jinhui Jeanne Huang

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Predictive Accuracy ◽

Water Environment ◽

Quality Parameters ◽

Machine Learning Algorithms ◽

Dynamic Monitoring ◽

Support Vector ◽

Seawater Quality

Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R2 = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R2 = 0.75 and 0.78 respectively) while DL performs better in TDS (R2 =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.

Download Full-text

Improved Training for Machine Learning: The Additional Potential of Innovative Algorithmic Approaches.

10.5194/egusphere-egu21-4683 ◽

2021 ◽

Author(s):

Octavian Dumitru ◽

Gottfried Schwarz ◽

Mihai Datcu ◽

Dongyang Ao ◽

Zhongling Huang ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Test Data ◽

Satellite Images ◽

Training Data ◽

Data Selection ◽

Generative Adversarial Networks ◽

Radar Images ◽

Basic Work ◽

Selection Of

During the last years, much progress has been reached with machine learning algorithms. Among the typical application fields of machine learning are many technical and commercial applications as well as Earth science analyses, where most often indirect and distorted detector data have to be converted to well-calibrated scientific data that are a prerequisite for a correct understanding of the desired physical quantities and their relationships.However, the provision of sufficient calibrated data is not enough for the testing, training, and routine processing of most machine learning applications. In principle, one also needs a clear strategy for the selection of necessary and useful training data and an easily understandable quality control of the finally desired parameters.At a first glance, one could guess that this problem could be solved by a careful selection of representative test data covering many typical cases as well as some counterexamples. Then these test data can be used for the training of the internal parameters of a machine learning application. At a second glance, however, many researchers found out that a simple stacking up of plain examples is not the best choice for many scientific applications.To get improved machine learning results, we concentrated on the analysis of satellite images depicting the Earth&#8217;s surface under various conditions such as the selected instrument type, spectral bands, and spatial resolution. In our case, such data are routinely provided by the freely accessible European Sentinel satellite products (e.g., Sentinel-1, and Sentinel-2). Our basic work then included investigations of how some additional processing steps &#8211; to be linked with the selected training data &#8211; can provide better machine learning results.To this end, we analysed and compared three different approaches to find out machine learning strategies for the joint selection and processing of training data for our Earth observation images:<ul><li>One can optimize the training data selection by adapting the data selection to the specific instrument, target, and application characteristics [1].</li> <li>As an alternative, one can dynamically generate new training parameters by Generative Adversarial Networks. This is comparable to the role of a sparring partner in boxing [2].</li> <li>One can also use a hybrid semi-supervised approach for Synthetic Aperture Radar images with limited labelled data. The method is split in: polarimetric scattering classification, topic modelling for scattering labels, unsupervised constraint learning, and supervised label prediction with constraints [3].</li> </ul>We applied these strategies in the ExtremeEarth sea-ice monitoring project (http://earthanalytics.eu/). As a result, we can demonstrate for which application cases these three strategies will provide a promising alternative to a simple conventional selection of available training data.[1] C.O. Dumitru et. al, &#8220;Understanding Satellite Images: A Data Mining Module for Sentinel Images&#8221;, Big Earth Data, 2020, 4(4), pp. 367-408.[2] D. Ao et. al., &#8220;Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X&#8221;, Remote Sensing, 2018, 10(10), pp. 1-23.[3] Z. Huang, et. al., "HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Images", IEEE Transactions on Geoscience and Remote Sensing, 2020, pp.1-18.

Download Full-text