- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments
摘要： The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models’ key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
关键词： paddy rice,machine learning,remote sensing,leaf area index,hyperspectral data
更新于2025-09-09 09:28:46
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - Wheat Drought Assessment by Remote Sensing Imagery Using Unmanned Aerial Vehicle
摘要： This work aims at evaluating the usability of remote sensing RGB imagery by an Unmanned Aerial Vehicle (UAV) in assessing wheat drought status. A UAV survey is conducted to collect high-resolution RGB imageries by using DJI S1000 for the experimental wheat ?elds of Gucheng town, Heibei Province, China. The soil moisture for different plots of the experimental ?led is kept at an approximately constant level for the whole growing season in a well controlled environment, where ?eld measurements are performed just after the UAV survey to obtain the soil water content for each plot. A machine learning based wheat drought assessment framework is proposed in this work. In the proposed framework, wheat pixels are ?rst segmented from the soil background using the classical normalized excess green index (NExG). Rather than using pixel-wise classi?cation, a pixel square of appropriate dimension is de?ned as the samples, based on which various features are extracted for the wheat pixels including statistical features and spectral index features. Different classi?cation algorithms are experimented to identify a suitable one in terms of classi?cation accuracy and computation time. It is discovered that Support Vector Machine with Gaussian kernel can obtain an accuracy over 90%, which demonstrates the usefulness of RGB imagery in wheat drought assessment.
关键词： UAV imagery,Wheat drought,Remote sensing,Classi?cation
更新于2025-09-09 09:28:46
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Assessment of Component Selection Strategies in Hyperspectral Imagery
摘要： Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.
关键词： hyperspectral sensor,remote sensing,texture measurement,classification,feature-extraction,ecosystem management
更新于2025-09-09 09:28:46
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Separating Crop Species in Northeastern Ontario Using Hyperspectral Data
摘要： The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400–900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400–900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75–79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.
关键词： soybean,wheat,barley,canola,oat,crop separability,hyperspectral remote sensing,optimal timing,precision agriculture
更新于2025-09-09 09:28:46
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Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
摘要： The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today’s transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.
关键词： Aerial imagery,wavelet transform,autonomous driving,traffic monitoring,remote sensing,fully convolutional neural networks (FCNNs),lane-marking segmentation,infrastructure monitoring,mapping
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 - A Hyperspectral LiDAR with Eight Channels Covering from VIS to SWIR
摘要： Hyperspectral LiDAR (HSL) possesses the advantages of the LiDAR and the hyperspectral detection, and detects ranging and spectrum information synchronously, by one HSL system. The data fusion is also avoided. At present, the spectrum range of reported HSLs usually covers only 500 nm-1000 nm (from visual (VIS) to near infrared (NIR) band). However, there is requirement to extend the spectrum range to short wave infrared (SWIR) band, which often contains more useful spectral information. In this paper, a HSL covering the spectrum from VIS to SWIR is reported. In the HSL, the echoes are divided into two sections and are detected by the different optoelectronic devices, of which the spectral response ranges are respectively compatible to the corresponding echoes. The HSL detection experiment in the laboratory was carried out. The waveforms of the echoes were analyzed, and the spectra of different targets were measured by the HSL. The experiment results demonstrate the capability of the prototyped HSL that obtaining the ranging information and the spectrum information of the targets in VIS-SWIR bands synchronously.
关键词： remote sensing,SWIR,supercontinuum laser,hyperspectral LiDAR
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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Assessing landscape scale heterogeneity in irrigation water use with remote sensing and in-situ monitoring
摘要： Understanding how irrigation is used across agricultural landscapes is essential to support efforts to grow more food while reducing pressures on limited freshwater resources. However, to date, few studies have analyzed the underlying spatial and temporal variability in farmers’ individual water use decisions at a landscape scale. We compare estimates of irrigation water requirements derived using state-of-the-art remote sensing models with metered abstraction records for 1,400 fields over a 13-year period in the U.S. state of Nebraska, one of the world’s most intensively irrigated agricultural regions. We show that farmers’ observed water use decisions often diverge significantly from biophysical estimates of crop irrigation requirements. In particular, our findings are consistent with widespread use of water conservation practices by farmers in drought years as an adaptive response to rising irrigation costs and regulatory water supply constraints in these years. We also demonstrate that, in any individual year, farmers’ observed water use exhibits large field-to-field variability, which cannot be explained fully by differences in weather, soil type, crop choice, or technology. Our results highlight the value of using both in-situ monitoring and remote sensing to evaluate farmers’ individual water use behavior and understand likely responses to future changes in climate or water policy. Moreover, our findings also demonstrate potential challenges for current efforts in developed and developing countries to apply model-based approaches for field-level water use accounting and enforcement of irrigation water rights.
关键词： Agriculture,Monitoring,Remote sensing,Water,Irrigation
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 - Deep Semi-Nonnegative Matrix Factorization Based Unsupervised Change Detection of Remote Sensing Images
摘要： In the paper, an unsupervised change detection method for remote sensing (RS) images based on deep semi-nonnegative matrix factorization (semi-NMF) is proposed. Firstly, the difference image is generated in different ways, depending on the types of input images. Then principal component analysis (PCA) is applied on the difference image to form the feature matrix X for improving the capability against various noise. In order to exploit more useful information from the resulting feature matrix, deep semi-NMF is introduced to factorize X into L+1 factors consisting of L nonrestricted matrices {Fl}Ll=1 and nonnegative cluster indicator matrix GL. Finally, the binary change mask (CM) is generated by assigning the pixels into changed and unchanged classes according to maximum criterion. The experimental results on two pairs of multitemporal RS images demonstrate the effectiveness of the proposed method.
关键词： remote sensing,principal component analysis,Unsupervised change detection,deep semi-nonnegative matrix factorization
更新于2025-09-09 09:28:46
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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor
摘要： The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16?day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.
关键词： pastures,time series,growth index,Zona da Mata,remote sensing
更新于2025-09-09 09:28:46