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- 实验方案
- 产品
过滤筛选
- 2018
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- deep learning
- remote sensing
- Moderate Resolution Imaging Spectroradiometer (MODIS)
- Optoelectronic Information Science and Engineering
- Ocean University of China
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Multi-resolution Image Fusion in Remote Sensing () || Introduction
摘要： One of the major achievements of human beings is the ability to record observational data in the form of photographs, a science which dates back to 1826. Humans have always tried to reach greater heights (treetops, mountains, platforms, and so on) to observe phenomenon of interest, to decide on habitable places, farming and such other activities. Curiosity motivates human beings to take photographs of the earth from elevated platforms. In the initial days of photography, balloons, pigeons, and kites were used to capture such photographs. With the invention of the aircraft in 1903, the ?rst aerial photograph on a stable platform was made possible in 1909 [120]. In the 1960s and 1970s, the primary platform that was used to carry remote sensing instruments shifted from aircraft to satellites [120]. It was during this period that the word ‘remote sensing’ replaced the frequently used word ‘aerial photograph’. Satellites can cover wider land space than planes and can monitor areas on a regular basis.
关键词： image fusion,remote sensing,satellites,aerial photograph,hyper-spectral images,multi-spectral images,panchromatic image
更新于2025-09-04 15:30:14
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Multi-resolution Image Fusion in Remote Sensing () || Literature Review
摘要： In many remote sensing applications, the spatial information of a fused image is as important as the spectral information. In other words, it is necessary to have images that have the spectral resolution of multi-spectral (MS) images and the spatial resolution of a panchromatic image. A sensor with spatial and spectral resolution, at the same time is hardly feasible [139]. The coarse spatial resolution of MS images is the result of a trade off due to physical and technical constraints. The quantity of light energy which arrives onto the detector is proportional to the width of its spectral range and hence, is smaller in the MS sensor than in the Pan sensor. It is therefore necessary to increase the energy that impinges onto the MS detector to obtain acceptable signal-to-noise ratio. However, this is not possible due to technological limitations. Further, if the MS images had high spatial resolution, the amount of data to transmit would be larger. The difficulties in on-board storage and data transmission to the ground also restrict the spatial resolution of MS images. This makes the remote sensing satellite sensors acquire MS images with low spatial resolution and the Pan image with high spatial resolution. Thus, the MS images have high spectral but low spatial resolution and the Pan image has high spatial but low spectral resolution.
关键词： image fusion,remote sensing,spectral resolution,spatial resolution,multi-spectral images,panchromatic image,Pan-sharpening
更新于2025-09-04 15:30:14
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Multi-resolution Image Fusion in Remote Sensing () || Image Fusion Using Different Edge-preserving Filters
摘要： In this chapter, we discuss fusion approaches using two edge-preserving filters, namely, guided filter and difference of Gaussians (DoGs). Since the MS and Pan images have high spectral and high spatial resolutions, respectively, one can obtain the resultant fused image using these two images by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for the extraction of high frequency details and also on the technique for injecting those details into the MS image. In the literature on multi-resolution image fusion, various approaches have been proposed based on the aforementioned process that also include state-of-the-art methods such as additive wavelet luminance proportional (AWLP) [178] and generalized Laplacian pyramid-context based decision (GLP-CBD) [13]. Motivated by these works, we first address the fusion problem by using different edge-preserving filters in order to extract the high frequency details from the Pan image. Specifically, we have chosen the guided filter and difference of Gaussians (DoGs) for detail extraction since these are more versatile in applications involving feature extraction, denoising, etc.
关键词： image fusion,multi-resolution,edge-preserving filters,remote sensing,guided filter,difference of Gaussians
更新于2025-09-04 15:30:14
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Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing
摘要： In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψstem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R2 ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day?1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.
关键词： thermal images,pistachio,almond,evapotranspiration,CWSI,remote sensing
更新于2025-09-04 15:30:14
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Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms
摘要： Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.
关键词： ensemble model,feature selection,time-series,MODIS and Landsat,remote sensing phenology,heavy metal stress
更新于2025-09-04 15:30:14