scholarly journals Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 5518
Author(s):  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
False Negative ◽  
Similarity Score ◽  
Majority Voting ◽  
Similarity Analysis ◽  
Spectral Angle Mapper ◽  
Mineral Detection ◽  
Shortwave Infrared

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

scholarly journals Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7313
Author(s):  
Honglyun Park ◽  
Namkyung Kim ◽  
Sangwook Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Spectral Distortion ◽  
Swir Band ◽  
Sentinel 2A ◽  
Types Of Information ◽  
Shortwave Infrared ◽  
Distortion Reduction

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.

scholarly journals A COMPARATIVE ASSESSMENT OF EFFICACY OF SUPER RESOLVED AIRBORNE HYPERSPECTRAL OUTPUTS IN URBAN MATERIAL AND LAND COVER INFORMATION EXTRACTION

2018 ◽  
Vol XLII-5 ◽  
pp. 653-658 ◽  
Author(s):  
K. Mishra ◽  
A. Siddiqui ◽  
V. Kumar
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Spatial Information ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Spectral Angle Mapper

<p><strong>Abstract.</strong> Urban areas despite being heterogeneous in nature are characterized as mixed pixels in medium to coarse resolution imagery which renders their mapping as highly inaccurate. A detailed classification of urban areas therefore needs both high spatial and spectral resolution marking the essentiality of different satellite data. Hyperspectral sensors with more than 200 contiguous bands over a narrow bandwidth of 1&amp;ndash;10<span class="thinspace"></span>nm can distinguish identical land use classes. However, such sensors possess low spatial resolution. As the exchange of rich spectral and spatial information is difficult at hardware level resolution enhancement techniques like super resolution (SR) hold the key. SR preserves the spectral characteristics and enables feature visualization at a higher spatial scale. Two SR algorithms: Anchored Neighbourhood Regression (ANR) and Sparse Regression and Natural Prior (SRP) have been executed on an airborne hyperspectral scene of Advanced Visible/Near Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for the mixed environment centred on Kankaria Lake in the city of Ahmedabad thereby bringing down the spatial resolution from 8.1<span class="thinspace"></span>m to 4.05<span class="thinspace"></span>m. The generated super resolved outputs have been then used to map ten urban material and land cover classes identified in the study area using supervised Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods. Visual comparison and accuracy assessment on the basis of confusion matrix and Pearson’s Kappa coefficient revealed that SRP super-resolved output classified using radial basis function (RBF) kernel based SVM is the best outcome thereby highlighting the superiority of SR over simple scaling up and resampling approaches.</p>

scholarly journals Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method

2020 ◽  
Vol 12 (16) ◽  
pp. 2664 ◽  
Author(s):  
Audrey Minghelli ◽  
Jérôme Spagnoli ◽  
Manchun Lei ◽  
Malik Chami ◽  
Sabine Charmasson
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
False Negative ◽  
Tohoku Earthquake ◽  
Validation Dataset ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Shoreline Evolution ◽  
Before And After

Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes.

scholarly journals Pre-launch calibration results of the TROPOMI payload on-board the Sentinel 5 Precursor satellite

2018 ◽  
Author(s):  
Quintus Kleipool ◽  
Antje Ludewig ◽  
Ljubiša Babic ◽  
Rolf Bartstra ◽  
Remco Braak ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Imaging System ◽  
Spectral Band ◽  
High Sensitivity ◽  
Radiometric Calibration ◽  
Random Errors ◽  
Synchronous Orbit ◽  
Shortwave Infrared ◽  
Ground Calibration

Abstract. The Sentinel 5 precursor satellite was successfully launched on 13th October 2017, carrying the Tropospheric Monitoring Instrument TROPOMI as its single payload. TROPOMI is the next generation atmospheric sounding instrument, continuing the successes of GOME, SCIAMACHY, OMI and OMPs, with higher spatial resolution, improved sensitivity and extended wavelength range. The instrument contains four spectrometers, divided over two modules sharing a common telescope, measuring the ultraviolet, visible, near-infrared and shortwave infrared reflectance of the Earth. The imaging system enables daily global coverage using a push-broom configuration, with a spatial resolution as low as 7 × 3.5 km2 in nadir from a Sun-synchronous orbit at 824 km and an equator crossing time of 13:30 local solar time. This article reports the pre-launch calibration status of the TROPOMI payload as derived from the on-ground calibration effort. Stringent requirements are imposed on the quality of on-ground calibration in order to match the high sensitivity of the instrument. In case that the systematic errors that originate from the calibration exceed the random errors in the observations, the scientific products may be compromised. A new methodology has been employed during the analysis of the obtained calibration measurements to ensure the consistency and validity of the calibration. This was achieved by using the production grade Level 0 to 1b data processor in a closed-loop validation setup. Using this approach the consistency between the calibration and the L1b product could be established, as well as confidence in the obtained calibration result. This article introduces this novel calibration approach, and describes all relevant calibrated instrument properties as they were derived before launch of the mission. For most of the relevant properties compliancy with the requirements could be established, including the knowledge of the instrument spectral and spatial response functions, and the absolute radiometric calibration. Partial compliancy was established for the straylight correction; especially the out-of-spectral-band correction for the NIR channel needs further validation. Incompliance was reported for the relative radiometric calibration of the Sun port diffusers. These latter two subjects will be addressed during the in-flight commissioning phase in the first 6 months following launch.

scholarly journals Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3313 ◽  
Author(s):  
Jasper de Meester ◽  
Tobias Storch
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Near Infrared ◽  
Atmospheric Effects ◽  
Spectral Bands ◽  
Multispectral Satellite Imagery ◽  
Radiation Sources ◽  
Yellow Orange ◽  
Band Combinations

Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.

scholarly journals Sentinel-2 Image Fusion Using a Deep Residual Network

2018 ◽  
Vol 10 (8) ◽  
pp. 1290 ◽  
Author(s):  
Frosti Palsson ◽  
Johannes Sveinsson ◽  
Magnus Ulfarsson
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
State Of The Art ◽  
Residual Network ◽  
Fused Image ◽  
Coarse Spatial Resolution ◽  
Single Sensor ◽  
Shortwave Infrared ◽  
Sentinel 2

Single sensor fusion is the fusion of two or more spectrally disjoint reflectance bands that have different spatial resolution and have been acquired by the same sensor. An example is Sentinel-2, a constellation of two satellites, which can acquire multispectral bands of 10 m, 20 m and 60 m resolution for visible, near infrared (NIR) and shortwave infrared (SWIR). In this paper, we present a method to fuse the fine and coarse spatial resolution bands to obtain finer spatial resolution versions of the coarse bands. It is based on a deep convolutional neural network which has a residual design that models the fusion problem. The residual architecture helps the network to converge faster and allows for deeper networks by relieving the network of having to learn the coarse spatial resolution part of the inputs, enabling it to focus on constructing the missing fine spatial details. Using several real Sentinel-2 datasets, we study the effects of the most important hyperparameters on the quantitative quality of the fused image, compare the method to several state-of-the-art methods and demonstrate that it outperforms the comparison methods in experiments.

scholarly journals SHARPENDING OF THE VNIR AND SWIR BANDS OF THE WIDE BAND SPECTRAL IMAGER ONBOARD TIANGONG-II IMAGERY USING THE SELECTED BANDS

2018 ◽  
Vol XLII-3 ◽  
pp. 1085-1092 ◽  
Author(s):  
Q. Liu ◽  
X. Li ◽  
G. Liu ◽  
C. Huang ◽  
H. Li ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Wide Band ◽  
Spectral Bands ◽  
Texture Information ◽  
Swir Band ◽  
Texture Characteristics ◽  
Spectral Imager ◽  
Shortwave Infrared ◽  
Band Image

The Tiangong-II space lab was launched at the Jiuquan Satellite Launch Center of China on September 15, 2016. The Wide Band Spectral Imager (WBSI) onboard the Tiangong-II has 14 visible and near-infrared (VNIR) spectral bands covering the range from 403&amp;ndash;990&amp;thinsp;nm and two shortwave infrared (SWIR) bands covering the range from 1230&amp;ndash;1250&amp;thinsp;nm and 1628&amp;ndash;1652&amp;thinsp;nm respectively. In this paper the selected bands are proposed which aims at considering the closest spectral similarities between the VNIR with 100&amp;thinsp;m spatial resolution and SWIR bands with 200&amp;thinsp;m spatial resolution. The evaluation of Gram-Schmidt transform (GS) sharpening techniques embedded in ENVI software is presented based on four types of the different low resolution pan band. The experimental results indicated that the VNIR band with higher CC value with the raw SWIR Band was selected, more texture information was injected the corresponding sharpened SWIR band image, and at that time another sharpened SWIR band image preserve the similar spectral and texture characteristics to the raw SWIR band image.

scholarly journals Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

2020 ◽  
Vol 9 (8) ◽  
pp. 478 ◽  
Author(s):  
Zemin Han ◽  
Yuanyong Dian ◽  
Hao Xia ◽  
Jingjing Zhou ◽  
Yongfeng Jian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Near Infrared ◽  
High Spatial Resolution ◽  
Land Cover Classification ◽  
Quantitative Comparison ◽  
Remote Sensing Imagery

Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future.

scholarly journals Classificação Espectral de Fitofisionomias em Área de Floresta Tropical Utilizando Dados do Sensor Aster

2019 ◽  
Vol 23 (2) ◽  
pp. 132
Author(s):  
Gustavo Manzon Nunes ◽  
Carlos Roberto De Souza Filho ◽  
Laerte Guimarães Ferreira ◽  
Luiz Eduardo Vicente ◽  
Maricéia Tatiana Vilani
Keyword(s):  
Sustainable Development ◽  
Near Infrared ◽  
Thermal Emission ◽  
Matched Filtering ◽  
Spectral Angle Mapper ◽  
Ndvi Index ◽  
Close Relationship ◽  
Spectral Patterns ◽  
Shortwave Infrared ◽  
Spectral Angle

Este artigo pretende avaliar a capacidade dos dados gerados pelo sensor Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)/Terra, na discriminação de fitofisionomias existentes na Reserva de Desenvolvimento Sustentável Amanã (RDSA). Os dados ASTER analisados incluem os intervalos espectrais do visível (0.52-0.69 μm), infravermelho próximo (0.78-0.86 μm) e infravermelho de ondas curtas (1.60 a 2.43 μm), sendo que nas bandas destes intervalos foram aplicadas técnicas de classificação espectral adaptadas para os dados deste sensor como Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF), além do NDVI. Através da técnica SAM foi possível a discriminação de seis fitofisionomias predominantes na RDSA. Através da técnica MTMF, que envolve um algoritmo de classificação mais robusto, informações equivalentes foram obtidas. Foi possível ainda a associação e detecção dos padrões espectrais da cobertura vegetal, mostrando a estreita relação com o índice NDVI. Palavras-chave: Mapeamento. Reserva de Desenvolvimento Sustentável Amanã. Vegetação.  Abstract This article aims to evaluate the data capacity created by a sensor named Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)/Terra, in the phytophysiognomies description of Amanã Sustainable Development Reserve (RDSA). The ASTER data analyzed include the spectral intervals of visible (0.52-0.69 μm), near-infrared (0.78-0.86 μm) and shortwave infrared (1.60 to 2:43 μm), wherein these intervals bands were applied the spectral classification techniques adapted to the data from this sensor as Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) plus NDVI. By SAM technique was possible to distinguish six predominant phytophysiognomies in the RDSA. By MTMF technique that involves a more robust classification algorithm, equivalent information was obtained. It was also possible to associate and detect spectral patterns of vegetation, showing the close relationship with the NDVI index. Keywords: Amanã Sustainable Development Reserve. Mapping. Vegetation. 

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