scholarly journals Urban Classification from Aerial and Satellite Images

2020 ◽  
Vol 10 (2) ◽  
pp. 163-172
Author(s):  
Iuliana Maria Pârvu ◽  
Iuliana Adriana Cuibac Picu ◽  
P.I. Dragomir ◽  
Daniela Poli
Keyword(s):  
Optical Sensors ◽  
Satellite Images ◽  
Near Infrared ◽  
Aerial Images ◽  
Support Vector ◽  
Multispectral Images ◽  
Spectral Bands ◽  
Urban Classification ◽  
Landsat 7 ◽  
Extract Information

AbstractWhen talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial images are generally acquired by optical sensors that deliver images in four bands (red, green, blue and near-infrared). Recent researches in this field demonstrated that for the image classification process is still place for improvement. From satellites are obtained multispectral images with more bands (e.g. Landsat 7/8 has 36 spectral bands). This paper will present the differences between these two types of images and the classification results using support-vector machine and maximum likelihood classifier. For the aerial and the satellite images we used different sets of classification classes and the two methods mentioned above to highlight the importance of choosing the classes and the classification method.

scholarly journals Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

2020 ◽  
Vol 12 (6) ◽  
pp. 993 ◽  
Author(s):  
Chen Yi ◽  
Yong-qiang Zhao ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
Resolution Enhancement ◽  
Multispectral Image ◽  
Observation Model ◽  
Multispectral Images ◽  
Spectral Matrix ◽  
Spectral Bands ◽  
Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

scholarly journals Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5481 ◽  
Author(s):  
Beatriz Martinez ◽  
Raquel Leon ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Juan F. Piñeiro ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Brain Cancer ◽  
Near Infrared ◽  
Relevant Information ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Spectral Bands ◽  
Tumor Identification ◽  
Spectral Ranges

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.

scholarly journals Informative value of ultra-high resolution satellite images for monitoring the moisture content of the tundra cover

2020 ◽  
pp. 82-95
Author(s):  
S.G. Kornienko
Keyword(s):  
High Resolution ◽  
Moisture Content ◽  
Satellite Images ◽  
Near Infrared ◽  
Yamal Peninsula ◽  
Ground Vegetation ◽  
Ndvi Index ◽  
Fundamental Possibility ◽  
High Resolution Satellite Images ◽  
Landsat 7

The article substantiates the fundamental possibility of using multispectral ultra-high spatial resolution satellite images for monitoring the moisture content of the tundra. The results of the analysis of spectral images from the QuickBird satellite in the area of the construction of the runway in the village Sabetta (the Yamal Peninsula) indicate an obvious relationship between the reflectance factors in the red (ρRED) and near infrared (ρNIR) regions with the types of terrain of varying degrees of drainage. The possibility of assessing changes in the moisture content of the tundra cover using high-resolution images is confirmed by the results of verifying the changes in ρRED, ρNIR and the NDVI index (according to the QuickBird and Ikonos satellites) by comparing with the changes in the NDWI index, which characterizes the cover moisture (according to the Landsat 7, 8 satellites). It is shown that the parameter ρRED is less sensitive, but it has an advantage over ρNIR and NDVI, since it changes unidirectionally with the changes in moisture for any encountered types of surface – from bare ground to developed ground vegetation cover with any real values of the NDVI index.

scholarly journals Landsat-7 ETM+ Based Remote Sensing as a Tool for Assessing Lakes Water Quality Characteristics

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Diego Fernando Cabezas-Alzate ◽  
Yeison Alberto Garcés-Gomez ◽  
Vladimir Henao-Cespedes
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Mathematical Models ◽  
Near Infrared ◽  
Multispectral Images ◽  
Chl A ◽  
Landsat 7 ◽  
Remote Sensing Techniques ◽  
Valle Del Cauca ◽  
Simple Ratio

The article describes a new method using remote sensing techniques to set the mathematical models that allow the estimation of the most relevant parameters for water quality monitored in Laguna de Sonso lake, Valle del Cauca, determined using Landsat-7 ETM+ multispectral images. Chlorophyll-a (Chl-a), Turbidity, Dissolved Oxygen (DO), and Total Phosphorus (P) are the parameters chosen for this study. The annual dry and wet seasons were defined, from 2010 to 2017, with a total of 70 images. It was necessary to carry out a process of masking the water Buchón (Eichhornia crassipes) and replacing pixels using the statistical average of the two established annual seasons. For the case of Chl-a, the NDI ratio between the red and near-infrared (NIR) bands was the best correlated with an ; for turbidity, a regression with the red band, with an ; for DO, the ratio with the highest correlation was a simple ratio (SR) between the green and blue bands, with an ; and for P, a regression of the NIR band was enough, presenting an . Finally, the adjusted mathematical models were obtained for each established parameter, allowing the estimation of each parameter to monitor the lagoon water quality using images from the ETM+ sensor.

scholarly journals CROP CLASSIFICATION ON SINGLE DATE SENTINEL-2 IMAGERY USING RANDOM FOREST AND SUPPOR VECTOR MACHINE

2018 ◽  
Vol XLII-5 ◽  
pp. 683-688 ◽  
Author(s):  
R. Saini ◽  
S. K. Ghosh
Keyword(s):  
Random Forest ◽  
Near Infrared ◽  
Vegetation Mapping ◽  
Good Choice ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tuning Parameters ◽  
Spectral Bands ◽  
Fodder Crops ◽  
Sentinel 2

<p><strong>Abstract.</strong> Mapping of the crop using satellite images is a challenging task due to complexities within field, and having the similar spectral properties with other crops in the region. Recently launched Sentinel-2 satellite has thirteen spectral bands, fast revisit time and resolution at three different level (10<span class="thinspace"></span>m, 20<span class="thinspace"></span>m, 60<span class="thinspace"></span>m), as well as the free availability of data, makes it a good choice for vegetation mapping. This study aims to classify crop using single date Sentinel-2 imagery in the Roorkee, district Haridwar, Uttarakhand, India. Classification is performed by using two most popular and efficient machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 satellite are stacked for the classification. Results show that overall accuracy of the classification achieved by RF and SVM using Sentinel-2 imagery are 84.22% and 81.85% respectively. This study demonstrates that both classifiers performed well by setting an optimal value of tuning parameters, but RF achieved 2.37% higher overall accuracy over SVM. Analysis of the results states that the class specific accuracies of High-Density Forest attain the highest accuracy whereas Fodder class reports the lowest accuracy. Fodder achieve lowest accuracy because there is an intermixing of pixels among Wheat and Fodder crops. In this study, it is found that RF shows better potential in classifying crops more accurately in comparison to SVM and Sentinel-2 has great potential in vegetation mapping domain in remote sensing.</p>

scholarly journals Melamine Faced Panels Defect Classification beyond the Visible Spectrum

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3644
Author(s):  
Cristhian Aguilera ◽  
Cristhian Aguilera ◽  
Angel Sappa
Keyword(s):  
Near Infrared ◽  
Visible Spectrum ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Feature Descriptor ◽  
Long Wavelength ◽  
Spectral Bands ◽  
Image Set ◽  
Long Wavelength Infrared

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.

scholarly journals Evaluation of Various Vegetation Indices for Multispectral Satellite Images

2019 ◽  
Vol 8 (10) ◽  
pp. 3494-3500
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Water Area ◽  
Multispectral Images ◽  
Predominant Role ◽  
Landsat Images ◽  
Spectral Bands ◽  
Different Types ◽  
Multispectral Satellite Images

Vegetation indices play a predominant role in the field of Remote processing systems which assimilate vital multispectral images. The digital numbers identify the spectral information in one or more spectral bands. It focuses mainly on two or more spectral regions and obtains different types of surfaces like vegetation, built-up, bare soil and water area. Different types of vegetation can be studied and analyzed using LANDSAT images. In this paper, comparison has been made on ten major vegetation indices such as RVI, DVI, NDVI, TNDVI, NDWI, MNDWI, NDBI, UI, SAVI, and NDMI using different spectral bands and different features are detected and extracted with the help of ArcGIS and MATLAB tools. This study reveals better classification accuracy.

scholarly journals Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

2019 ◽  
Vol 11 (11) ◽  
pp. 1298 ◽  
Author(s):  
Ahmed Laamrani ◽  
Aaron A. Berg ◽  
Paul Voroney ◽  
Hannes Feilhauer ◽  
Line Blackburn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Southern Ontario ◽  
Spectral Bands ◽  
Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

scholarly journals Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1411
Author(s):  
Yanchao Zhang ◽  
Wen Yang ◽  
Ying Sun ◽  
Christine Chang ◽  
Jiya Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multispectral Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Spectral Bands ◽  
Vegetation Indexes

Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.

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