- 标题
- 摘要
- 关键词
- 实验方案
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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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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 - Investigation of Natural Ecological Enviroment Using Remote Sensing Based Integrated Index at a City Scale
摘要： It is of great importance to monitor and evaluate the dynamics of ecological environment due to severe human activities. In our study, three remote sensing based ecological factors were selected to generate an integrated index for estimating the natural ecological environment at a city scale, specifically including vegetation coverage, soil index and slope. Vegetation coverage was derived from Normalized Difference Vegetation Index (NDVI) of Landsat 8 OLI (Operational Land Imager) imagery. Soil index was generated by the greenness above bare soil (GRABS). Slope was derived from 30 meters resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). An integrated index with the method of weighting average was obtained by normalizing the three indicators. The results show that Hefei has a relatively good ecological environment. The ratio of excellent and good levels accounts for 71.00% of the study area. In general, the ecological environment is better in the southern part than that in the northern part.
关键词： NDVI,Integrated index,Remote sensing,Landsat 8 OLI,Ecological environment
更新于2025-09-10 09:29:36
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Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8 – A case study in Quito, Ecuador
摘要： The Andean region has a high cloud density throughout the year. The use of optical remote sensing data in the computation of environmental indices of this region has been hampered by the presence of clouds. To maximize accuracy in the computation of several environmental indices including the normalized difference vegetation index (NDVI), we compared the performance of two algorithms in removing clouds in Landsat-8 Operational Land Imager (OLI) data of a high-elevation area. The study area was Quito, Ecuador, which is a city located close to the equator and in a high-elevation area crossed by the Andes Mountains. The first algorithm was the automatic cloud removal method (ACRM), which employs a linear regression between the different spectral bands and the cirrus band. The second algorithm was independent component analysis (ICA), which considers the noise (clouds) as part of independent components applied over the study area. These methods were evaluated based on several images from different years with different cloud conditions. The results indicate that neither algorithm is effective over this region for the removal of clouds or for NDVI computation. However, after improving ACRM, the NDVI computed using ACRM showed a better correlation than ICA with the MODIS NDVI product.
关键词： Quito,optical remote sensing,cloud removal,NDVI,Landsat-8 OLI
更新于2025-09-10 09:29:36
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In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
摘要： The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.
关键词： Red Green Ratio Index (RGRI),Normalized Difference Flood Index (NDFI),COSMO-SkyMed,Random Forest,Enhanced Vegetation Index (EVI),multi-temporal,summer crops,Landsat 8 OLI,rule-based classification,agriculture
更新于2025-09-10 09:29:36
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Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images
摘要： Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.
关键词： GF-4 PMS,cross-calibration,SCE-UA,data assimilation,Landsat-8 OLI,BRDF
更新于2025-09-09 09:28:46
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Using new remote sensing satellites for assessing water quality in a reservoir
摘要： Water quality monitoring could benefit from information derived from the newest generation of medium resolution Earth observation satellites. The main objective of our study was to assess the suitability of both Landsat 8 and Sentinel-2A satellites for estimating and mapping Secchi disk transparency (SDT), a common measurement of water clarity, in Cassaffousth Reservoir (Córdoba, Argentina). Ground observations and a dataset of four Landsat 8 and four Sentinel-2A images were used to create and validate models to estimate SDT in the reservoir. The selected algorithms were used to obtain graphic representations of water clarity. Slight differences were found between Landsat 8 and Sentinel-2 estimations. Thus, we demonstrated the suitability of both satellites for estimating and mapping water quality. This study highlights the importance of free and readily-available satellite datasets in monitoring water quality especially in countries where conventional monitoring programs are either lacking or unsatisfactory.
关键词： water clarity,Secchi disk depth,monitoring,Sentinel-2 MSI,remote sensing,Landsat 8 OLI
更新于2025-09-09 09:28:46
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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 - Spatial Algal Bloom Characterization by Landsat 8-Oli and Field Data Analysis
摘要： Water pollution is an important problem around the world as it is closely related to human and environmental health. Field campaigns are expensive, time consuming and may provide little information. Remote sensing provides synoptic spatio-temporal views and can lead to a better understanding of lake ecology. In this work an extreme algal bloom event which occurred in a reservoir is characterized by LANDSAT 8-OLI sensor and in situ sampling. Chlorophyll-a concentration and algae abundance data are measured on samples collected simultaneously with satellite pass and used to build semiempirical models. Two linear functions to calculate chlorophyll-a from satellite data are presented and compared. A linear model from band 2 (blue) and band 5 (NIR) presents the best performance with a determination coefficient equal to 0,89. In situ and satellite chlorophyll-a lead comparable trophic class assessment, hypertrophic. Both Models fail to predict chlorophyll-a concentration near river intrusion (North), where low values of reflectance are recorded.
关键词： linear regression,chlorophyll-a,phytoplankton,LANDSAT 8-OLI,eutrophication
更新于2025-09-09 09:28:46
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Imager-to-radiometer in-flight cross calibration: RSP radiometric comparison with airborne and satellite sensors
摘要： This work develops a method to compare the radiometric calibration between a radiometer and imagers hosted on aircraft and satellites. The radiometer is the airborne Research Scanning Polarimeter (RSP), which takes multi-angle, photo-polarimetric measurements in several spectral channels. The RSP measurements used in this work were coincident with measurements made by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), which was on the same aircraft. These airborne measurements were also coincident with an overpass of the Landsat 8 Operational Land Imager (OLI). First we compare the RSP and OLI radiance measurements to AVIRIS since the spectral response of the multispectral instruments can be used to synthesize a spectrally equivalent signal from the imaging spectrometer data. We then explore a method that uses AVIRIS as a transfer between RSP and OLI to show that radiometric traceability of a satellite-based imager can be used to calibrate a radiometer despite differences in spectral channel sensitivities. This calibration transfer shows agreement within the uncertainty of both the various instruments for most spectral channels.
关键词： cross calibration,Landsat 8 OLI,radiometric calibration,AVIRIS,RSP
更新于2025-09-04 15:30:14
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[IEEE 2018 3rd International Conference for Convergence in Technology (I2CT) - Pune (2018.4.6-2018.4.8)] 2018 3rd International Conference for Convergence in Technology (I2CT) - Fire Detection in a Varying Topography Using Landsat-8 for Nainital Region, India
摘要： Forest fires are the most frequent phenomenon during the summer season in India, and especially in the hilly terrains of Uttarakhand forests. Remote sensing sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Visible Infrared Imaging Radiometer Suite (VIIRS) with coarse spatial resolution on board different satellites were used to detect the forest fires across the world. Landsat-8 Operational Land Imager (OLI) data has the better spatial resolution (30m) as compared with the MODIS and VIIRS, therefore useful to detect the smaller fires. Nainital district in Uttarakhand state was severely affected by the massive forest fire events occurred during April-May, 2016. The main objective of the study is to identify the potential of Landsat-8 data in detecting the forest fire for varying topographic region like Nainital. Landsat-8 data acquired on 28th April 2016 and 1st May 2016 has been used in this study. The results obtained from Landsat-8 data are compared with the MODIS fire products and showed an improvement in the detection of small fires.
关键词： MODIS,fire algorithm,Landsat-8 OLI,Forest fire,VIIRS
更新于2025-09-04 15:30:14