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- 实验方案
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Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties
摘要： The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s?1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.
关键词： remote sensing accuracy,inland waters,optically complex systems
更新于2025-09-23 15:23:52
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Advancing the PROSPECT-5 Model to Simulate the Spectral Reflectance of Copper-Stressed Leaves
摘要： This paper proposes a modified model based on the PROSPECT-5 model to simulate the spectral reflectance of copper-stressed leaves. Compared with PROSPECT-5, the modified model adds the copper content of leaves as one of input variables, and the specific absorption coefficient related to copper (Kcu) was estimated and fixed in the modified model. The specific absorption coefficients of other biochemical components (chlorophyll, carotenoid, water, dry matter) were the same as those in PROSPECT-5. Firstly, based on PROSPECT-5, we estimated the leaf structure parameters (N), using biochemical contents (chlorophyll, carotenoid, water, and dry matter) and the spectra of all the copper-stressed leaves (samples). Secondly, the specific absorption coefficient related to copper (Kcu) was estimated by fitting the simulated spectra to the measured spectra using 22 samples. Thirdly, other samples were used to verify the effectiveness of the modified model. The spectra with the new model are closer to the measured spectra when compared to that with PROSPECT-5. Moreover, for all the datasets used for validation and calibration, the root mean square errors (RMSEs) from the new model are less than that from PROSPECT-5. The differences between simulated reflectance and measured reflectance at key wavelengths with the new model are nearer to zero than those with the PROSPECT-5 model. This study demonstrated that the modified model could get more accurate spectral reflectance from copper-stressed leaves when compared with PROSPECT-5, and would provide theoretical support for monitoring the vegetation stressed by copper using remote sensing.
关键词： vegetation remote sensing,leaf,PROSPECT,reflectance model,copper,spectra
更新于2025-09-23 15:23:52
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A change detection framework by fusing threshold and clustering methods for optical medium resolution remote sensing images
摘要： In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximization (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogenous CD map but with some missed detections. Therefore, a framework is designed to improve CD results by fusing the advantages of the threshold and clustering methods. The CD map generated by the clustering method of FLICM is used to remove false alarms in the CD map obtained by EM threshold method by an overlap fusion. Then, the local Markov random field model is implemented to verify the potentially missed detections. Finally, a fused CD map with less false alarms and missed detections is achieved. Two experiments were carried out on two Landsat ETM+ data sets. The proposed method obtained the least errors (1.11% and 3.51%) and the highest kappa coefficient (0.9366 and 0.8834), respectively, when compared with five popular CD methods.
关键词： Change detection,advantage fusion,remote sensing,clustering,threshold
更新于2025-09-23 15:23:52
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Scale-variable region-merging for high resolution remote sensing image segmentation
摘要： In high resolution remote sensing imagery (HRI), the sizes of different geo-objects often vary greatly, posing serious difficulties to their successful segmentation. Although existent segmentation approaches have provided some solutions to this problem, the complexity of HRI may still lead to great challenges for previous methods. In order to further enhance the quality of HRI segmentation, this paper proposes a new segmentation algorithm based on scale-variable region merging. Scale-variable means that the scale parameters (SP) adopted for segmentation are adaptively estimated, so that geo-objects of various sizes can be better segmented out. To implement the proposed technique, 3 steps are designed. The first step produces a coarse-segmentation result with slight degree of under segmentation error. This is achieved by segmenting a half size image with the global optimal SP. Such a SP is determined by using the image of original size. In the second step, structural and spatial contextual information is extracted from the coarse-segmentation, enabling the estimation of variable SPs. In the last step, a region merging process is initiated, and the SPs used to terminate this process are estimated based on the information obtained in the second step. The proposed method was tested by using 3 scenes of HRI with different landscape patterns. Experimental results indicated that our approach produced good segmentation accuracy, outperforming some competitive methods in comparison.
关键词： Image segmentation,High resolution remote sensing imagery,Scale-variable,Region merging
更新于2025-09-23 15:23:52
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Study of the effects of phytoplankton morphology and vertical profile on lidar attenuated backscatter and depolarization ratio
摘要： Propagation of a lidar beam in a coupled atmosphere-ocean model consisting of multiple atmospheric and upper oceanic layers and a rough ocean surface is studied by using a vectorized Monte Carlo radiative transfer solver optimized specifically for lidar-based remote sensing applications. The effects of assumed phytoplankton morphology variations and its vertical distribution on the lidar attenuated backscatter and depolarization ratio are studied. In this study, a phytoplankton particle is assumed to be a sphere, a sphere with a core, or a randomly distorted hexahedron with or without a core. The single-scattering properties of the nonspherical/inhomogeneous particles are computed using appropriate state-of-the-art light-scattering computational capabilities. Vertical variation of the phytoplankton distribution is derived explicitly using a PAR (photosynthetically active radiation) limited carbon biomass balance equation that is subsequently coupled with the Monte Carlo solver.
关键词： Radiative transfer,Lidar,Ocean optics,Monte Carlo,Phytoplankton,Net primary production,Remote sensing
更新于2025-09-23 15:23:52
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Remote sensing images super-resolution with deep convolution networks
摘要： Remote sensing image data have been widely applied in many applications, such as agriculture, military, and land use. It is difficult to obtain remote sensing images in both high spatial and spectral resolutions due to the limitation of implements in image acquisition and the law of energy conservation. Super-resolution (SR) is a technique to improve the resolution from a low-resolution (LR) to a high-resolution (HR). In this paper, a novel deep convolution network (DCN) SR method (SRDCN) is proposed. Based on hierarchical architectures, the proposed SRDCN learns an end-to-end mapping function to reconstruct an HR image from its LR version; furthermore, extensions of SRDCN based on residual learning and multi scale version are investigated for further improvement, namely Developed SRDCN(DSRDCN) and Extensive SRDCN(ESRDCN). Experimental results using different types of remote sensing data (e.g., multispectral and hyperspectral) demonstrate that the proposed methods outperform the traditional sparse representation based methods.
关键词： Convolution neural network,Remote sensing imagery,Super-resolution
更新于2025-09-23 15:23:52
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A top-down approach for semantic segmentation of big remote sensing images
摘要： The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.
关键词： Neural networks,Remote sensing images,Big data,Semantic segmentation
更新于2025-09-23 15:23:52
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Aerosol uncertainty assessment: an integrated approach of remote AQUA MODIS and AERONET data
摘要： The moderate resolution imaging spectroradiometer (MODIS) is one of the widely used sensors to address environmental and climate change subjects with a daily global coverage. MODIS Collection 6 aerosol products at 10-km resolution are used in this study to monitor aerosol variability and assess its uncertainty using ground-based measurements. The aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. Using data collected from Sidi Salem Aerosol Robotic Network (AERONET) station, we computed the accuracy for aerosol optical depth (AOD) retrieved from MODIS aboard the AQUA satellite using two validation methods. The results show a good agreement between MODIS and AERONET data for the study period using both the algorithms. We obtained high values of the correlation coefficient. These findings indicate that MODIS data perform well over Ben Salem AERONET station and are recommended for air quality monitoring over Tunisia. The conducted validation throughout the AERONET leads to a degree of confidence that allows a deep investigation of the AOD spatial variability over Tunisia. Then, MODIS data shows high performance with good certainty to identify the principal dust sources and typical transport paths occurring on the study region.
关键词： AQUA,Remote sensing,AOD,AERONET,Aerosol,MODIS
更新于2025-09-23 15:23:52
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Automatic bathymetry retrieval from SAR images
摘要： Bathymetry, the topography of the sea floor, is in high demand due to the increase in offshore constructions like wind parks. It is also an important dataset for climate change modelling, when sea level rises and changes in circulation currents are to be simulated. The retrieval of accurate bathymetry data is a cost-intensive task usually requiring a survey vessel charting the respective area. However, bathymetry can also be retrieved remotely using data from Earth observation satellites. The main point of this study is the development of a processor that allows the automatic derivation of gridded bathymetry information from spaceborne Synthetic Aperture Radar (SAR) data. Observations of sea state modifications in SAR images are used to derive the bathymetry in shelf areas using the shoaling effect, which causes wavelengths to become shorter when reaching shallower waters. The water depth is derived using the dispersion relation for surface water waves, which requires wavelength and wave period as input parameters. While the wavelength can be directly retrieved from the SAR image, for the peak period additional information and procedures are required, e.g. local measurements or complex SAR data. A method for automatically deriving the wave period for swell waves in SAR images was developed and tested in this paper. It uses depth data from public databases as initial values which are compared to derived depths iterating through possible peak periods along the calculation grid; the peak period resulting in a minimal root-mean-square deviation is then used for bathymetry calculation. The bathymetry derived from a TerraSAR-X acquisition of the Channel Islands is presented; the resulting peak wave period of 11.3 s fits well to nearby in situ measurement data.
关键词： Bathymetry,Remote sensing,Near-real time processing,Synthetic aperture radar
更新于2025-09-23 15:23:52
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Detection of land use/cover change in Egyptian Nile Delta using remote sensing
摘要： The present study aims to assess the changes of different land use/land cover classes for Nile Delta of Egypt during the period from 1987 to 2015, to evaluate the impact of land cover change and urban sprawl, before, during and after the 25th of January 2011 using remote sensing and GIS techniques, as a result to unplanned urban sprawl which was done by people during the lack of general security of Egyptian revolution. The results indicated that there was a regular trend characterized in most classes and that the change in different land use/land cover classes ranged between increase and decrease areas. A continuous increase in agricultural, urban, ?sh farms and natural vegetation areas and a continuous decrease in water bodies and sand areas were detected in the studied area. The agricultural area recorded the highest increase during the period from 1987 to 2000 (305296.1 ha.) while it increased by 170578.1 ha., during the period from 2000 to 2015. However, in urban area, the highest increase was recorded during the period from 2000 to 2015 followed by the period 1987–2000 with mean values of 97940.8 and 53112.6 ha., respectively. The analysis of the results showed that most of Egyptian Delta governorates have been signi?cantly affected by the different classes of land use/land cover change due to agriculture activities, urban growth as a result of human activities dynamic impact.
关键词： Change detection,Remote sensing,Nile Delta governorates,Land use/land cover
更新于2025-09-23 15:23:52