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[IEEE 2017 Ninth International Conference on Advanced Computing (ICoAC) - Chennai, India (2017.12.14-2017.12.16)] 2017 Ninth International Conference on Advanced Computing (ICoAC) - A Framework for Quality Enhancement of Multispectral Remote Sensing Images
摘要： Researches in satellite image enhancement have been particularly con?ned to two major areas- contrast enhancement and image denoising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images.
关键词： Enhancement,Contrast,Remote sensing
更新于2025-09-04 15:30:14
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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 - Remote Sensing Using VNIR/SWIR Dispersive Imaging Spectrometers: Historical Development, Current State-of-the-Art, and Furture Trends
摘要： The development of remote sensing using an imaging spectrometer is traced from its origins to the current highly performing spectral sensors. The advancement of the technology has primarily been driven by novel optical designs and focal plane array development. Current state-of-the-art dispersive sensors, employing innovative grating or prism arrangements, operate at low F-numbers while maintaining a high degree of aberration control. These challenging designs have been implemented in hardware with alignment tolerances on the order of microns. The application of imaging spectrometers to science problems that require highly accurate radiometry necessitates new characterization approaches that combine state-of-the-art radiometric sources and detailed sensor models. Future challenges are to reduce the size, weight, and power requirements while maintaining the performance of current systems.
关键词： VNIR/SWIR,optical designs,remote sensing,imaging spectrometer,focal plane array
更新于2025-09-04 15:30:14
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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 - The Use of a Quadcopter-Mounted Hyper-Spectral Spectrometer for Examining Reflectance in Scottish Coastal Waters
摘要： Remote sensing of the marine coastal environment is useful for obtaining information about processes occurring within it. Monitoring has traditionally been carried out in situ, before investment increased in remote techniques such as manned planes and satellites. This paper proposes the use of remotely piloted aircraft systems (RPAS) as an alternative platform, with an aim of increasing the spectral, spatial and temporal resolution of data whilst reducing the associated costs and risks. A custom-built ‘spectro-copter’ system, comprising of an integrated dual field-of-view, miniaturised, hyper-spectral spectrometer aboard a purpose-built quadcopter is presented, developed at the Scottish Association for Marine Science (SAMS), Oban. This has been produced with a view to investigating reflectance from Scottish coastal waters, which can give inferences as to the concentrations of various constituents present [1, 2]. Initial test flights show the ‘spectro-copter’ system is capable of flights of ~20 minutes, sufficient for meaningful data collection, despite late adjustments incurring increased weight and an associated reduction in efficiency. Early results demonstrate that the setup is capable of discerning differences in R at a high spectral resolution. Further work is ongoing in order to assess the capacity for establishing this equipment as a routine technique for the monitoring of coastal harmful algal blooms (HAB), which currently relies upon physical sampling in combination with satellites. HAB events are noted to be increasing in severity and frequency [3, 4], with knock-on health and economic impacts, particularly for the rising aquaculture industry in Scottish coastal waters [5]. Therefore advancement of an alternative, affordable technique would be extremely beneficial.
关键词： spectrometer,Scottish coastal waters,reflectance,remote-sensing,quadcopter,remotely piloted aircraft system,hyper-spectral
更新于2025-09-04 15:30:14
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Improving Hypersharpening for WorldView-3 Data
摘要： In this letter, hypersharpening is analyzed in depth by investigating some weaknesses present in its formulation. It is shown that the key formula of the synthesized band variant can be simplified under certain circumstances. In addition, a novel fusion schema is proposed. As a result, the gain factor adopted to weight the injected detail is computed in a different way. This schema can be applied to fuse a wide range of hyperspectral and multispectral data. In this letter, its effectiveness is demonstrated by taking into account the characteristics of WorldView-3 data.
关键词： pansharpening,image fusion,WorldView-3,remote sensing,Hypersharpening
更新于2025-09-04 15:30:14
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Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
摘要： We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intraclass variance while maximizing the interclass separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the nonleaf nodes of the tree. We employ an iterative maximum-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced data sets demonstrate the efficacy of the proposed hierarchical metric learning-based RS scene recognition strategy in comparison to the standard approaches.
关键词： optical remote sensing (RS),Maximum margin clustering (MMC),metric learning
更新于2025-09-04 15:30:14
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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