Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data

2021 ◽  
Vol 87 (5) ◽  
pp. 349-362
Author(s):  
Shalini Gakhar ◽  
K.C. Tiwari
Keyword(s):  
Machine Learning ◽  
Target Detection ◽  
City Planning ◽  
Urban Landscape ◽  
Relevant Information ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Distance Measures ◽  
Information Measures ◽  
Detection Algorithms

Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience—distance measures, angle-based measures, information measures, and machine-learning measures—followed by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

scholarly journals Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

2020 ◽  
Vol 9 (9) ◽  
pp. 507
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Stuart Phinn
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forest Height ◽  
Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

scholarly journals Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

2019 ◽  
Vol 11 (22) ◽  
pp. 2605 ◽  
Author(s):  
Wang ◽  
Chen ◽  
Wang ◽  
Li
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Soil Salinity ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Percentage Error ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Data Noise ◽  
Salt Affected Soil

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

scholarly journals Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension Reduction Approach

2019 ◽  
Author(s):  
Nicholas Westing ◽  
Brett Borghetti ◽  
Kevin Gross
Keyword(s):  
Machine Learning ◽  
Radiative Transfer ◽  
Dimension Reduction ◽  
Real Time ◽  
Computational Cost ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Atmospheric Effects ◽  
Atmospheric Compensation ◽  
The Impact

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.

Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

2014 ◽  
Author(s):  
Alessandro Rossi ◽  
Nicola Acito ◽  
Marco Diani ◽  
Giovanni Corsini ◽  
Sergio Ugo De Ceglie ◽  
...  
Keyword(s):  
Data Collection ◽  
Target Detection ◽  
Hyperspectral Data ◽  
Detection Algorithms

scholarly journals Qmin: A machine learning-based application for mineral chemistry data processing and analysis

2021 ◽  
Author(s):  
Guilherme Ferreira da Silva ◽  
Marcos Vinicius Ferreira ◽  
Iago Sousa Lima Costa ◽  
Renato Borges Bernardes ◽  
Carlos Eduardo Miranda Mota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Relevant Information ◽  
Mineral Chemistry ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Max Planck ◽  
Blind Test ◽  
Chemistry Data ◽  
Learning Techniques

Abstract Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

scholarly journals Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

2021 ◽  
Vol 11 (22) ◽  
pp. 10628
Author(s):  
John Chauvin ◽  
Ray Duran ◽  
Kouhyar Tavakolian ◽  
Alireza Akhbardeh ◽  
Nicholas MacKinnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Spectral Data ◽  
Near Infrared ◽  
Tunable Filter ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Imaging Systems ◽  
Large Set ◽  
Scanning Method

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications.

Sign in / Sign up

Export Citation Format

Share Document