A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images

A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale.

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Development and Application of Quality Control System for Highway Subgrade Construction

智能城市应用 ◽

10.33142/sca.v2i5.814 ◽

2019 ◽

Vol 2 (5) ◽

Author(s):

Tong Wang

Keyword(s):

Quality Control ◽

Real Time ◽

Detection Method ◽

System Development ◽

Effective Control ◽

Construction Process ◽

Development Mode ◽

The Road ◽

Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

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Valuation mapping of hazards of the crisis natural and economic situations

Geodesy and Cartography ◽

10.22389/0016-7126-2017-929-11-40-49 ◽

2017 ◽

Vol 929 (11) ◽

pp. 40-49

Author(s):

N.E. Krasnoshtanova ◽

A.K. Cherkashin

Keyword(s):

Spatial Data ◽

Heterogeneous Information ◽

Secondary Use ◽

Raster Maps ◽

Irkutsk Region ◽

Homogeneous Network ◽

Surrounding Areas ◽

Integral Assessment ◽

Multidimensional Statistics

An innovative technique for the secondary use of cartographic information for creating assessment hazard maps of crisis natural and economic situations and an integral assessment of the sustainability economic development and the quality of live is presented. Valuation mapping was carried for the Slyudyansky district of the Irkutsk region. A database has been created for homogeneous network of plots, which contains heterogeneous information about the nature and socio-economic environment of the district. Spatial data were processed using multidimensional statistics on the base of reliability theory models. An account of the environmental correction for each plots is an important aspect of the proposed technique of assessing and creating through maps. This makes it possible to reduce the evaluation function to an invariant form common to all locations and it is used in through way to create assessment maps for natural and socio-economic objects. As a result, a series of raster maps of through thematic content was made. The map of integral hazard of emergence of economic crisis situation displays the lowest hazard values for the territories of settlements and their surrounding areas, as well as areas along roads and railways. Additionally it allocates undeveloped valley of taiga rivers, advanced for economic use, primarily for recreational purposes.

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Ensemble-Based Out-of-Distribution Detection

Electronics ◽

10.3390/electronics10050567 ◽

2021 ◽

Vol 10 (5) ◽

pp. 567

Author(s):

Donghun Yang ◽

Kien Mai Mai Ngoc ◽

Iksoo Shin ◽

Kyong-Ha Lee ◽

Myunggwon Hwang

Keyword(s):

Detection Method ◽

State Of The Art ◽

Metric Learning ◽

Feature Space ◽

Confidence Score ◽

Distance Metric Learning ◽

Current State ◽

Overall Performance ◽

Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

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International Institutions and the Environmental Crisis: A Look Ahead

International Organization ◽

10.1017/s0020818300003404 ◽

1972 ◽

Vol 26 (2) ◽

pp. 469-478 ◽

Cited By ~ 3

Author(s):

David A. Kay ◽

Eugene B. Skolnikoff

Keyword(s):

Urban Areas ◽

International Institutions ◽

Modern Technology ◽

Environmental Crisis ◽

Widespread Application ◽

Human Environment ◽

Look Ahead ◽

International Issue ◽

Surrounding Areas

In the industrialized northern hemisphere we are assaulted daily with evidence of the deteriorating quality of the human environment: Rivers are closed to fishing because of dangerous levels of contamination; the safety of important foods is challenged; the foul air that major urban areas have been forced to endure is now spreading like an inkblot into surrounding areas. Lack of early concern about the implications for the environment of the widespread application of modern technology has allowed the problem to grow rapidly into a critical domestic and international issue.

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Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Remote Sensing ◽

10.3390/rs13122301 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2301

Author(s):

Zander Venter ◽

Markus Sydenham

Keyword(s):

Land Cover ◽

Atmospheric Correction ◽

Google Earth ◽

Tree Planting ◽

Training Data ◽

Classification Model ◽

Night Time ◽

Continental Scale ◽

Land Cover Maps ◽

Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

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Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

Remote Sensing ◽

10.3390/rs13020320 ◽

2021 ◽

Vol 13 (2) ◽

pp. 320

Author(s):

José P. Granadeiro ◽

João Belo ◽

Mohamed Henriques ◽

João Catalão ◽

Teresa Catry

Keyword(s):

Large Scale ◽

Exposure Period ◽

Intertidal Area ◽

Digital Elevation ◽

Geographical Bias ◽

Novel Method ◽

Tidal Stage ◽

Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

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Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

Applied Sciences ◽

10.3390/app11094258 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4258

Author(s):

Jordan R. Cissell ◽

Steven W. J. Canty ◽

Michael K. Steinberg ◽

Loraé T. Simpson

Keyword(s):

Google Earth ◽

Mangrove Forests ◽

Random Forest Classification ◽

High Resolution Mapping ◽

Mangrove Ecosystems ◽

Forest Classification ◽

Resolution Mapping ◽

Google Earth Engine ◽

Terrestrial Biodiversity ◽

Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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Analysis of the industrial quality of three sugarcane cultivars at “santa rosalía de la chontalpa” sugarcane mill

RECUBRIMIENTO COMESTIBLE CON BASE DE PIÑON MEXICANO (Jatropha curcas) SOBRE LA CALIDAD DE CHAYOTE MINIMAMENTE PROCESADO ◽

10.32854/agrop.v14i1.1940 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Samuel Córdova-Sánchez ◽

José Izquierdo-Hernández ◽

Sergio Salgado-García ◽

Luz del Carmen Lagunes-Espinoza ◽

David Jesús Palma-López ◽

...

Keyword(s):

Design Methodology ◽

Reducing Sugars ◽

Environmental Friendly ◽

Linear Polynomial ◽

Sugar Mills ◽

Standard Values ◽

Santa Rosalia ◽

The One ◽

Best Fit

Objective: To evaluate the industrial quality of three sugarcane cultivars in a template cycle at the supply area of “Santa Rosalía de la Chontalpa” sugarcane mill. Design / Methodology / Approach: An experiment was established under a factorial design 3x3 (3 cultivars: CP 72-2086, MEX 79-431 and MEX 69-290; x 3 sampling dates: 330, 390 and 450 DDS, Spanish equivalent for days after sowing) on an Eutric Fluvisol soil. In each plantation, a sample of 10 stems with three replications was collected to determine the industrial quality by polarimetry. Results: The industrial quality of the evaluated cultivars only differed statistically in terms of the percentage of purity, MEX 79-431 was the one that presented the lowest value for this variable. At 450 DDS, the highest value was observed for °Brix (17.28), POL percentage (14.92), purity (86.44%). The values obtained in the present study for the quality of juice in the evaluated cultivars are within the range of the standard values established for Mexico. Limitations / Implications: Polarimetry is still the method used by most of the sugar mills in Mexico, even if other more environmental-friendly methodologies exist. Findings / Conclusions: The trend line that best fit to MEX 69-290 and MEX 79-431, for °Brix, POL and purity, was a linear polynomial and to CP 72-2086, a polynomial quadratic. Fresh stems and reducing sugars showed best fit with an inverse polynomial. °Brix presented strong and positive correlation with POL (R = 0.99**); and strong and negative with reducer sugars (R = -0.95**) and fresh stem humidity (R = -0.91**).

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