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- 实验方案
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- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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Densely connected deep random forest for hyperspectral imagery classification
摘要： In very recent years, deep learning based methods have been widely introduced for the classification of hyperspectral images (HSI). However, these deep models need lots of training samples to tune abundant parameters which induce a heavy computation burden. Therefore, most of these algorithms need to be accelerated with high-performance graphics processing units (GPU). In this paper, a new deep model–densely connected deep random forest (DCDRF) is proposed to classify the hyperspectral images. This model is composed of multiple forward connected random forests. The DCDRF has following merits: 1) It obtains satisfactory classification accuracy with a small number of training samples, 2) It can be run efficiently on the central processing unit (CPU), 3) Only a few parameters are involved during the training. Experimental results based on three hyperspectral images demonstrate that the proposed method can achieve better classification performance than the conventional deep learning based methods.
关键词： DCDRF,hyperspectral imagery classification,random forest,deep learning,densely connected deep random forest
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 - Hyperspectral Retrieval of Canopy Water Content Through Inversion of the Beer-Lambert Law
摘要： The retrieval of quantitative equivalent water thickness on canopy level (EWTc) is an agriculturally important task for hyperspectral remote sensing. In this study the Beer-Lambert law is applied to inversely determine water content from measured winter wheat spectra collected in 2015 and 2017. The spectral model is calibrated using a look-up-table (LUT) of 50.000 PROSPECT spectra. Validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS) and in-situ data acquired in Southern Germany. After considering destructive in-situ water content measurements separately for leaves, stems, and fruits, results indicate optically active plant water by plant component in the 930 to 1060 nm range of canopy reflectance. Results for spectrally derived EWTc were most promising for leaves and ears reaching coefficients of determination up to 0.75 and a normalized RMSE (nRMSE) of 24% between measured and estimated canopy water content.
关键词： EnMAP,hyperspectral,agriculture,canopy water content,spectroscopy
更新于2025-09-04 15:30:14
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Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS
摘要： Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (Poa secunda) from invasives such as burr buttercup (Ranunculus testiculatus) in a shrubland environment. The test flights successfully acquired usable images. Unsupervised flightline data capable of supporting classifiable composite classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup.
关键词： Management,Unmanned,Imaging spectroscopy,Sagebrush,Monitoring,Hyperspectral,drones,Fixed-wing,Vegetation
更新于2025-09-04 15:30:14
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[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Hyperspectral Image: Fundamentals and Advances
摘要： Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more ef?cient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classi?cation techniques and the most recently popular classi?cation algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter.
关键词： image enhancement,restoration,Hyperspectral imaging,classification,remote sensing,denoising,datasets
更新于2025-09-04 15:30:14
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Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data
摘要： Quantitative equivalent water thickness on canopy level (EWTcanopy) is an important land surface variable and retrieving EWTcanopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWTcanopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWTcanopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWTcanopy in the scope of future hyperspectral satellite missions.
关键词： EnMAP,hyperspectral,spectroscopy,equivalent water thickness,canopy water content,agriculture
更新于2025-09-04 15:30:14
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A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements
摘要： A method for canopy Fluorescence Spectrum Reconstruction (FSR) is proposed in this study, which can be used to retrieve the solar-induced canopy fluorescence spectrum over the whole chlorophyll fluorescence emission region from 640–850 nm. Firstly, the radiance of the solar-induced chlorophyll fluorescence (Fs) at five absorption lines of the solar spectrum was retrieved by a Spectral Fitting Method (SFM). The Singular Vector Decomposition (SVD) technique was then used to extract three basis spectra from a training dataset simulated by the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes). Finally, these basis spectra were linearly combined to reconstruct the Fs spectrum, and the coefficients of them were determined by Weighted Linear Least Squares (WLLS) fitting with the five retrieved Fs values. Results for simulated datasets indicate that the FSR method could accurately reconstruct the Fs spectra from hyperspectral measurements acquired by instruments of high Spectral Resolution (SR) and Signal to Noise Ratio (SNR). The FSR method was also applied to an experimental dataset acquired in a diurnal experiment. The diurnal change of the reconstructed Fs spectra shows that the Fs radiance around noon was higher than that in the morning and afternoon, which is consistent with former studies. Finally, the potential and limitations of this method are discussed.
关键词： Fluorescence Spectrum Reconstruction (FSR),solar-induced chlorophyll fluorescence (Fs),Spectral Fitting Method (SFM),Fraunhofer Line Discriminator (FLD),hyperspectral remote sensing,Singular Vector Decomposition (SVD)
更新于2025-09-04 15:30:14
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Comprehensive Remote Sensing || Advanced Feature Extraction for Earth Observation Data Processing
摘要： The Earth is a highly complex and dynamic network system, and in the last few hundred years, human activities have precipitated many important changes. It goes without saying that the biggest challenge that we, as scientists, are facing nowadays is to quantify, predict, and understand this system’s behavior. For example, land and vegetation monitoring has deep societal, environmental, and economical implications, especially with the rapidly growing demand of biofuels and food. We need data and models to make inferences on the system. These models should provide not only predictions but also qualitative explanations about when, where, and how much the variables impact the observations.
关键词： Earth observation,kernel methods,hyperspectral images,principal curves,remote sensing,deep learning,feature extraction
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 - DIM Moving Target Detection using Spatio-Temporal Anomaly Detection for Hyperspectral Image Sequences
摘要： Dim moving target detection from hyperspectral image sequences, which contains temporal information as well as spectral information, has attracted researchers’ interest for its crucial role in civil and military application. In this paper, we propose a novel spatio-temporal anomaly approach to solve the dim moving target detection problem. This approach calculates spatial anomaly map, temporal anomaly map using anomaly detection algorithm from spatial domain and temporal domain, respectively. To achieve motion consistency characteristic, this approach manages to generate the trajectory prediction map. After fusing the spatial anomaly map, the temporal anomaly map and the trajectory prediction map, target of interest can be easily detected from background. The proposed approach is applied to a test dataset of airborne target in the cloud clutter background. Experimental results confirm that the proposed approach can achieve a low false alarm rate as well as a high probability of detection.
关键词： Hyperspectral imagery sequences,Spatial and temporal processing,Anomaly detection,Dim target detection
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 - Local Similarity Regularized Sparse Representation for Hyperspectral Image Super-Resolution
摘要： Recently, performance of hyperspectral image super-resolution (SR) has been significantly improved via sparse representation. However, most of these existing methods fail to consider the local geometrical structure of the sparse coefficients. To take this crucial issue into account, this paper proposes an effective method, which exploits the location related constraint about the sparse coefficients and incorporates their local similarity into the sparse coding process. Thus, the proposed method can preserve the properties of the aforementioned local geometrical structures. Based on the experimental results, the proposed method is demonstrated to be more effective than previous efforts in the task of hyperspectral image SR.
关键词： Local similarity,Sparse representation,Hyperspectral image,Super-resolution
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 - Deep Auto-Encoder Network for Hyperspectral Image Unmixing
摘要： In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder network composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a variational auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic dataset. In our comparison with other state-of-the-art unmixing methods, the proposed approach demonstrates highly competitive performance.
关键词： Variational auto-encoder,Hyperspectral unmixing,Non-negative sparse auto-encoder,Deep learning
更新于2025-09-04 15:30:14