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- 摘要
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- 实验方案
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A New Semi-Automatic Seamless Cloud-Free Landsat Mosaicing Approach Tracks Forest Change Over Large Extents
摘要： The extensive and freely available archive of Landsat satellite data is used throughout the world to assess forest changes over large areas and long time periods (30-40 years). But analyzing Landsat data in time-series is not free of challenges (e.g. data processing and storage capabilities, dealing with cloud cover and other data gaps, and accounting for changes in illumination conditions due to atmospheric effects, sun angle and vegetation phenology). In this research, we present a method used to create annual seamless cloud-free mosaics for the entire state of Victoria, Australia (19 Landsat tiles), for a 30 year period. These mosaics were created by first constructing yearly Best Available Pixel (BAP) composites from over 3000 individual scenes. Then, forested areas were analyzed in time-series to determine breakpoints (e.g. a disturbance event such as fire). Following this, the breakpoints were used to fit a piece-wise linear regression model through each pixel’s temporal trajectory. In this way, data gaps and other radiometric anomalies were removed. These gap-free composites can be used by a variety of stakeholders for land management, statutory reporting and decision making activities. This ensures state-wide consistency, and offers significant savings in processing and storage requirements.
关键词： Compositing,Landsat,Time Series
更新于2025-09-23 15:22:29
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A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples
摘要： Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency’s (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod.
关键词： classification,CNN,feature engineering,built-up area,Landsat 8-OLI,accuracy evaluation,feature learning
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Geological Mapping of Hydrothermal Alteration on Volcanoes from Multi-Sensor Platforms
摘要： Hydrothermal alteration due to geothermal fluids often introduces mineral alteration and weathering that poses significant natural hazards around volcanoes. Hydrothermal alteration can be mapped remotely using satellite and airborne derive images. In this study, we explored the capacity of available multispectral satellites, high-resolution airborne hyperspectral and LiDAR imagery to provide an improved geological mapping and classification capability for volcanic terrains. Image classification experiments using a Random Forest approach trained using ground class data to classify 15 ground cover types show that Sentinel-2 and Landsat 8 OLI+TIR can provide a geological map with Overall (OA) and Kappa Accuracies (KA) of 69% and 66% respectively. Classification accuracy was dramatically improved when high-resolution airborne datasets were included. The use of full-spectrum AisaFENIX hyperspectral images improved accuracies to OA = 84% and KA = 82%. The maximum image classification accuracy is reached (OA = 87, KA = 85%) when all input features were combined.
关键词： Sentinel-2,volcano,geological mapping,Landsat 8,LiDAR,hyperspectral imaging
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Exploring Sentinel-L Data for Local Climate Zone Classification
摘要： Local climate zone (LCZ) is a categorical scheme describing the morphology of urban area, which is a valuable not only for the original purpose of temperature study, but also for other urban oriented studies like population density estimation and economical development monitoring. Standard LCZ production works only on individual cities using merely optical data, mostly LandSat-8 data. Our goal is to develop a framework that 1) can potentially work on a large number of cities, i.e., training on number of cities and testing on number of other cities; 2) exploits Synthetic Aperture Radar (SAR) data. In this paper, we investigated the potential of Sentinel-1 Dual-Pol data on producing LCZ maps in general. It shows the Sentinel-1 data could improve the classification accuracy of several LCZ classes. Joint use of LandSat-8 data, OpenStreetMap (OSM) data and Sentinel-1 data provide 62.05% over-all accuracy, which is higher than 51.20% achieved by using only LandSat-8 and OSM data.
关键词： Local Climate Zone (LCZ),LandSat-8,Sentinel-1,Classification,Dual-Pol
更新于2025-09-23 15:21:21
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Wideband 4 ?? 4 Butler Matrix in The Printed Ridge Gap Waveguide Technology for Millimeter Wave Applications
摘要： An underground nuclear explosion (UNE) can generate a shock wave that lofts surface material, resulting in surface changes that might be detectable. The Comprehensive Nuclear Test-Ban Treaty (CTBT) allows ground and airborne spectral and thermal imaging to help locate such events. Landsat 5 data on the 1998 Indian and Pakistani tests are used here to demonstrate that there are detectable changes in surface features which might be used to localize an underground nuclear test and to develop change detection techniques speci?c to the use of satellite data to support a CTBT on-site inspection. Landsat 5 has been active for over 20 years providing repeat coverage of the Earth’s surface every 16 days. Most locations have Landsat data available for a variety of dates, allowing for statistical analysis of the data to understand temporal trends and data variability on a pixel-by-pixel basis. Given the right conditions, these usual patterns of change (such as seasonal changes or weathering) can be discerned from unusual patterns of change, such as features relating to a UNE. This paper extends known change detection techniques to a temporal series of data and shows that multispectral change detection can be used to help localize a UNE.
关键词： multispectral change detection,Comprehensive Nuclear Test-Ban Treaty (CTBT),covariance matrix Landsat 5,Mahalanobis distance
更新于2025-09-23 15:21:01
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Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2
摘要： Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
关键词： MNDWI,OSH,SVM,AWEI,water mapping,water classification,shadow,NDWI,Sentinel-2,MuWI,Landsat,water index,multi-spectral water index,sunglint,machine learning
更新于2025-09-23 15:21:01
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Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine
摘要： Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
关键词： suspended sediment,Landsat,water quality,extreme learning machine,machine learning
更新于2025-09-23 15:21:01
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Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection
摘要： Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.
关键词： SPOT,co-registration,Landsat,optical satellite data,multi-temporal,RapidEye,accuracy,ASTER,landslide,Kyrgyzstan
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Integrated Optical Fibers for Simultaneous Monitoring of the Anode and the Cathode in Lithium Ion Batteries
摘要： A fully automatic phenology-based synthesis (PBS) classification algorithm was developed to map land cover based on medium spatial resolution satellite data using the Google Earth Engine cloud computing platform. Vegetation seasonality, particularly in the tropical dry regions, can lead conventional algorithms based on a single date image classification to “misclassify” land cover types, as the selected date might reflect only a particular stage of the natural phenological cycle. The PBS classifier operates with occurrence rules applied to a selection of single date image classifications of the study area to assign the most appropriate land cover class. Since the launch of Landsat 8 in 2013, it has been possible to acquire imagery at any point on the Earth every 16 days with exceptional radiometric quality. The relatively high global acquisition frequency and the open data policy allow near-real-time land cover mapping and monitoring with automated tools such as the PBS classifier. We mapped four protected areas and their 20-km buffer zones from different ecoregions in Sub-Saharan Africa using the PBS classifier to present its first results. Accuracy assessment was carried out through a visual interpretation of very high resolution images using a Web geographic information system interface. The combined overall accuracy was over 90%, which demonstrates the potential of the classifier and the power of cloud computing in geospatial sciences.
关键词： Google Earth Engine (GEE),image classification,phenology,land cover mapping,Landsat 8
更新于2025-09-23 15:19:57
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Internal Waves on River Plumes
摘要： We present results of observations of internal waves (IW) on the river plumes caused by the spread of the front of fresh waters, based on satellite images of the visible range obtained by Sentinel-2A Multispectral Imager Instrument (MSI/S2) and Landsat-8 Operational Land Imager (OLI/L8) instruments. Due to the high spatial resolution of these satellite data, submesoscale IWs having wavelengths less than 50 m and generated by unstable sharp front of a river plume, were revealed and their parameters were assessed. The plumes of the following rivers were studied: the Rh?ne, flowing into the Gulf of Lyon of the Mediterranean Sea, the Danube, flowing into the northwestern part of the Black Sea and the Coruh flowing into the southeastern part of the Black Sea. We discuss spatio-temporal variability of the manifestations of internal waves of a given type mechanisms of their generation.
关键词： OLI Landsat-8,river plume,coastal zones,Internal waves,MSI Sentinel-2
更新于2025-09-23 15:19:57