- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment
摘要： Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre- and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none–slight, slight–heavy, heavy–destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse.
关键词： damage assessment,Landsat-8,ALOS-2 interferometric coherence,urban damage area,liquefaction,landslides,Kumamoto earthquake
更新于2025-09-23 15:23:52
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Enhancing multispectral remote sensing data interpretation for historical gold mines in Egypt: a case study from Madari gold mine
摘要： In the last decade, most of the outcrops around the historic gold mines in Egypt had been damaged by the local miners, a situation that complicated remote sensing-based exploration research activities. Madari gold mine area was no more fortunate than other mines in the region. This study identifies a new integrated remote sensing workflow that emphasizes the spectral variations related to differences in chemical and mineralogical compositions of the investigated rock units and deemphasizes the spectral variations introduced by the local miners. All combinations of ratio images are first generated from Landsat 8 Operational Land Imager (OLI) data, then a suite of ratio images that best differentiates between the investigated units is selected, and finally the selected ratio images were stacked to substitute the original image bands in the further processing techniques. The PCA was then applied to the selected ratio images within the stack. Subsequently, a statistical analysis of the eigenvector matrix for each of the PC bands was conducted to select the optimum PC bands and a Principal Component False Color Composite image (PC-FCC) was created from the three selected PC bands. The PC-FCC image (PC3, PC11, PC4 in RGB) was chosen as a result of subtracting the average PC eigenvector negative weights from the average positive eigenvectors weights. Not only was the PC-FCC image used to distinguish the main rock units in the damaged area, but also, to identify the areas with intense alteration zones.
关键词： Eastern Desert,Principal component analysis (PCA),Landsat 8 (OLI),Madari gold mine,Egypt,Ratio images
更新于2025-09-23 15:23:52
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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 - Investigating the Relationship Between Shallow Groundwater, Soil Moisture and Land Surface Temperature Using Remotely Sensed Data
摘要： Shallow groundwater has a decisive impact on land surface temperature (LST) and soil moisture (SM). In the present paper relationship between shallow groundwater, SM and LST was studied. For this purpose, the groundwater level and soil moisture were measured in 59 and 39 locations respectively in the southwest of Iran, during June 2016, Simultaneous with the overpass of a Landsat 8 satellite from the study site. After necessary image processing the LST was retrieved from the Landsat image using the split window algorithm. Then relationship between retrieved LST and different field observation were studied. Results show that there is a significant relationship between the groundwater depth and SM with LST. These results indicate that shallow groundwater depth and soil moisture content could be estimated and mapped using the retrieved LST from the satellite imagery.
关键词： Remote Sensing,LST,Landsat 8,Shallow Groundwater,Soil moisture
更新于2025-09-23 15:23:52
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Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for Urban Impervious Surface Mapping
摘要： Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination (R2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.
关键词： Landsat-8,Heterogeneous features,Sentinel-1A,multiple kernel support vector regression (MKSVR),impervious surface abundance
更新于2025-09-23 15:23:52
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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 - Towards Joint Land Cover and Crop Type Mapping with Numerous Classes
摘要： The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type maps using a highly analytic nomenclature of more than 40 classes. An intensive manual annotation procedure was carried out for the production of reference data. A class nomenclature based on CORINE land cover Level-3 was employed along with several additional crop-type classes. Multitemporal surface reflectance Landsat-8 data for the year of 2016 were used for all classification experiments with a linear SVM classifier. Quantitative and qualitative evaluation highlighted the efficiency of the proposed approach achieving high accuracy rates. Further analysis on individual classes’ performance highlighted the challenges in the proposed classification scheme as well as important outcomes regarding the spectral behavior of the considered categories.
关键词： support vector machines,CORINE Land Cover,Landsat-8,classification
更新于2025-09-23 15:23:52
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[IEEE 2018 14th International Conference on Emerging Technologies (ICET) - Islamabad, Pakistan (2018.11.21-2018.11.22)] 2018 14th International Conference on Emerging Technologies (ICET) - Identification and mapping of coral reefs using Landsat 8 OLI in Astola Island, Pakistan coastal ocean
摘要： Recent field surveys have reported the presence of corals in many places in the Pakistan coastal ocean; Astola Island especially has been a subject of interest with regards to corals and overall marine biodiversity, and has in fact recently been declared Pakistan’s first Marine Protected Area. This study presents an analysis of coral reefs identification and their spatial distribution through optical satellite remote sensing in the surrounding area of Astola Island. Besides remote sensing data, the study considers sea survey data collected by divers in recent years. A benthic map of ocean ecosystem habitats is generated, through processing of Landsat 8 OLI (Operational Land Imager) imagery. The satellite data was selected at low-tide time to get maximum sunlight penetration in shallow water. Water-column correction was used to generate the depth-invariant index on multiple band-pairs. Water column corrected depth-invariant index bands were then segmented and classified through object-based classification. The results from the remote sensing data processing over Astola Island show good agreement with the field survey data, with nearly all the field survey points of coral reefs falling within the coral reefs class. Use of remote sensing imagery such as Landsat 8, and application of the water column correction method can allow for regular monitoring and management of coral reefs and other benthic ecosystems in the coastal ocean of Pakistan and coastal Arabian Sea.
关键词： Pan-sharpening,Landsat 8,Coral reefs,Object Base Image Analysis,Depth Invariant Index
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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[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