Application Of Hyperspectral Data Research Articles

Hyperspectral Image Study based on Deep Learning Model HSI-ConvNext

In recent years, deep learning techniques have made significant research progress in the field of hyperspectral image processing. Hyperspectral images are widely used in agriculture, environmental monitoring, geological exploration and other fields due to their richness in material and spectral information about objects. Deep learning models play an important role in hyperspectral image research and are able to learn feature representations from large-scale high-dimensional data, improving the performance of tasks such as classification, segmentation, and detection. In the study, researchers usually improve and optimize for the features of hyperspectral images by constructing deep learning models such as HSI-ConvNext. It is able to capture the correlation between different bands, thus realizing semantic segmentation and target detection for hyperspectral images. Data augmentation can effectively expand the limited hyperspectral dataset, and migration learning is able to migrate the knowledge of models trained in other domains to hyperspectral image tasks. Realistic hyperspectral images can be generated for data enhancement and model training. In conclusion, the study of hyperspectral images based on the deep learning model HSI-ConvNext provides new methods and techniques for the analysis and application of hyperspectral data. In the future, with the continuous development of deep learning technology, we can expect more innovations and breakthroughs in the field of hyperspectral image processing.

Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data.

APPLICATION OF AIRBORNE DATA TO MONITOR URBAN INFRASTRUCTURE OBJECTS

Abstract. The paper demonstrates the application of hyperspectral data of Flight Laboratory of Imaging Systems (FLIS, https://olc.czechglobe.cz/en) to participate in three practical tasks of urban environment in the Czech Republic: 1/identification of asbestos-cement roofs on buildings; 2/monitoring of thermal regime of municipal solid waste landfill, and 3/estimation of perceived temperature in different parts of the city. These applications benefited from the airborne hyperspectral methods' ability to collect detailed spectral information, assess material composition, and extract biophysical parameters.

Joint Texture Search and Histogram Redistribution for Hyperspectral Image Quality Improvement

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data. This paper proposes a quality enhancement algorithm based on texture search and histogram redistribution combined with denoising and contrast enhancement. Firstly, a texture-based search algorithm is proposed to improve the accuracy of denoising by improving the sparsity of 4D block matching clustering. Then, histogram redistribution and Poisson fusion are used to enhance spatial contrast while preserving spectral information. Synthesized noising data from public hyperspectral datasets are used to quantitatively evaluate the proposed algorithm, and multiple criteria are used to analyze the experimental results. At the same time, classification tasks were used to verify the quality of the enhanced data. The results show that the proposed algorithm is satisfactory for hyperspectral data quality improvement.

Performance of hyperspectral data in predicting and mapping zinc concentration in soil

Reflectance spectroscopy in visible, near-infrared, and short-wave infrared (VNIR-SWIR) region has been recognized as a promising alternative for prediction of heavy metal concentration in soil. Compared with VNIR-SWIR reflectance spectroscopy, VNIR reflectance spectroscopy is less affected by atmospheric water vapor and has relatively high signal to noise ratio. The performances of VNIR and VNIR-SWIR hyperspectral data in predicting and mapping heavy metal concentration in soil were explored. In this study, laboratory spectra of soil samples collected from an agricultural area and Advanced Hyperspectral Imaging (AHSI) remote sensing imagery were used to predict and map zinc (Zn) concentration with genetic algorithm and partial least squares regression (GA-PLSR). The entire spectral regions of VNIR-SWIR and VNIR and spectral subsets extracted from the entire spectral regions were used in the prediction. For the laboratory spectra, the combination of the spectral bands extracted from the absorption features at 500 nm and in 600–800 nm obtained the highest prediction accuracy with the root mean square error (RMSE) and coefficient of determination (R2) values of 8.90 mg kg−1 and 0.72. For soil spectra from AHSI remote sensing imagery, the highest prediction accuracy was achieved by using the spectral bands extracted from the absorption feature in 600–800 nm with the RMSE and R2 values of 9.02 mg kg−1 and 0.75. Soil Zn concentration maps were generated with the established prediction models using AHSI remote sensing imagery. Analysis on the Zn concentration maps shows that the prediction model established using the spectral bands extracted from the absorption feature in 600–800 nm has a better performance in mapping Zn concentration. The results indicate that VNIR hyperspectral data outperforms VNIR-SWIR hyperspectral data in predicting and mapping Zn concentration in soil, which provides an alternative to the application of hyperspectral data in soil science.

Automatic detection of Flavescense Dorée grapevine disease in hyperspectral images using machine learning

The technological revolution that we have been witnessing recently has allowed components miniaturization and made electronic components accessible. Hyperspectral sensors benefited from these advances and could be mounted on unmanned aerial vehicles, which was unthinkable until recently. This fact significantly increased the applications of hyperspectral data, namely in agriculture, especially in the detection of diseases at an early stage. The vineyard is one of the agricultural sectors that has the most to gain from the use of this type of data, both by the economic value and by the number of diseases the plants are exposed to. The Flavescense dorée is a disease that attacks vineyards and may conduct to a significant loss. Nowadays, the detection of this disease is based on the visual identification of symptoms performed by experts who cover the entire area. However, this work remains tedious and relies only on the human eye, which is a problem since sometimes healthy plants are torn out, while diseased ones are left. If the experts think they have found symptoms, they take samples to send to the laboratory for further analysis. If the test is positive, then the whole vine is uprooted, to limit the spread of the disease. In this context, the use of hyperspectral data will allow the development of new disease detection methods. However, it will be necessary to reduce the volume of data used to make them usable by conventional resources. Fortunately, the advent of machine learning techniques empowered the development of systems that allow better decisions to be made, and consequently save time and money. In this article, a machine learning approach, which is based on an Autoencoder to automatically detect wine disease, is proposed.

Assessment of Water Stress in Vineyards Using On-The-Go Hyperspectral Imaging and Machine Learning Algorithms

<b>Highlights</b> <list list-type=bullet><list-item> Many vineyards are located in arid and semi-arid climatic conditions with dry summers. </list-item><list-item> Optimal water supply is crucial to avoid any yield or quality loss due to under-irrigation or over-irrigation, respectively. </list-item><list-item> Hyperspectral images, weather data, and machine learning models were used to classify water stress into three categories. </list-item><list-item> Optimal random forest classifier (RFC) and artificial neural network (ANN) models had accuracies of 73% and 70%, respectively. </list-item></list> <b>Abstract</b>. Efficient use of scarce water resources for optimum crop yield and quality is a major concern in dry lands. Traditionally, in-situ methods such as measurements of leaf water potential (ΨL) or soil moisture have been used for estimating plant water stress in vineyards. However, these methods are time, labor, and cost-intensive, and limited to point measurements of water stress that do not offer much spatial resolution. In this study, hyperspectral images acquired from a ground-based utility vehicle were analyzed to evaluate the applicability of hyperspectral data in classifying plant water stress measured as ΨL into three classes: no to mild water stress (ΨL > −0.8 MPa), moderate water stress (-0.8 MPa ≤ ΨL ≤ -1.2 MPa) and severe water stress (ΨL < -1.2 MPa). A field trial was carried out in an experimental vineyard in arid southeastern Washington, USA. Grapevines were subjected to either full irrigation (FI) or regulated deficit irrigation (RDI). Several vegetative indices (VIs) derived from hyperspectral reflectance information were assessed to estimate the level of water stress. Linear relationships between VIs and ΨL, and relative variable importance based on Gini impurity in different test models were evaluated to select optimal VIs for vine water stress classification. Green Normalized Difference Vegetation Index (GNDVI), Photochemical Reflectance Index (PRI), and Anthocyanin Index (ANT) showed consistently good trends in the data obtained over two growing seasons and were selected as the optimal VIs. In addition, air relative humidity, evapotranspiration, minimum soil temperature, and solar radiation demonstrated high significance in model development and were included in the final optimized random forest classifier (RFC) and artificial neural network (ANN) models. The RFC model had an accuracy of 73% while the ANN model had an accuracy of 70% in classifying plant water condition into three classes. Both models demonstrated good classification abilities and can be applied to analyze vine water stress and developing decision support tools for precision irrigation in vineyards.

Multiscale DenseNet Meets With Bi-RNN for Hyperspectral Image Classification

Convolutional neural network (CNN) has been successfully introduced to hyperspectral image (HSI) classification and achieved effective performance. With the depth of the CNN increases, it may cause the gradient to become zero, and the structure lacks the utilization of the correlated spatial feature information between different convolutional layers. At the same time, this single-scale convolution kernel is insufficient in expressing the complex spatial structure information of HSI. Additionally, the CNN-based methods treat the HSIs spectral band data as a disordered vector in the process of feature extraction, which abandons the exploitation of its internal spectral correlations. To address these issues, we propose a novel spectral-spatial network classification framework based on multi-scale dense connected convolutional network (DenseNet) and bi-direction recurrent neural network (Bi-RNN) with attention mechanism network (MDRN). For the proposed MDRN, in terms of spatial feature extraction, a multi-scale DenseNet is exploited to combine shallow and deep convolution features to extract the multi-scale and complex spatial structure features at each layer. In the aspects of spectral feature extraction, Bi-RNN with attention mechanism is used to capture the inner spectral correlations within a continuous spectrum. Three standard real hyperspectral datasets were used to verify the effectiveness of the proposed MDRN approach. Experimental results indicate that the proposed MDRN method can make full use of the spectral and spatial information of the image, and it has better performance than some advanced algorithms in HSI classification. Finally, in the application of hyperspectral data captured by Gaofen-5 (GF-5) satellite, the practicability of the proposed MDRN method is also superior to other methods.

Deep learning-based crop mapping in the cloudy season using one-shot hyperspectral satellite imagery

Crop mapping is essential for agricultural management, economic development planning, and ecological conservation. Remote sensing with a large field of view provides us with a potential technique for large-scale crop mapping. However, most of the previous studies have focused on multi-temporal crop mapping, requiring multiple imaging over a period of time, which is impossible in the cloudy season due to the absence of clear atmospheric windows. Recently, with the progress of spaceborne hyperspectral imaging technology, wide-width hyperspectral satellite images, which provide abundant spectral and spatial structure information, have made the precision crop mapping of large areas possible. This paper focuses on deep learning based crop mapping using one-shot hyperspectral satellite imagery, where three convolutional neural network (CNN) models, i.e., 1D-CNN, 2D-CNN, and 3D-CNN models, are applied for end-to-end crop mapping. In addition, a manifold learning based visualization approach, i.e., t-distributed stochastic neighbor embedding (t-SNE), is introduced to illustrate the discriminative ability of the deep semantic features extracted by the different CNN models. To demonstrate the advantages of one-shot hyperspectral satellite images, an experiment was designed to compare the crop mapping performance of different remote sensing data sources, where both mono-temporal and multi-temporal multispectral images (MSIs) of the same research area were introduced for a systematic comparison. The classification accuracy when using hyperspectral satellite images was found to reach more than 94%, which was much better than that when using mono-temporal MSIs, and was comparable to the result when using multi-temporal MSIs. These findings will be important for the application of hyperspectral data when mapping large-area crop landscapes, and they confirm the potential of CNN models, particularly 3D-CNN models, for crop recognition.

Integrated software design of dispersive spectrometer imaging system

Hyperspectral imaging technology is a new means of ground observation technology, which can simultaneously obtain the geometric image of the target and spectral information. It is the main instrument of high spectral remote sensing data acquisition. In order to facilitate the analysis of the motion imaging degradation mechanism of dispersive spectrometer and the high-precision real-time correction effect, we have developed a software system integrating motion imaging degradation of dispersive spectrometer, restoration correction and quality evaluation. Given the powerful image data processing function of Matlab and C# written form of the structure of the interface height flexibility and convenience, this system is compiled with the C# and Matlab mixed mode. Practical applications show that the software system has the characteristics of reasonable structure, complete functions, stable performance, and open system. It provides technical support for establishing a fast hyperspectral high-precision restoration correction processing platform and improving the accuracy of hyperspectral data, which will speed up the practical application of hyperspectral data.

Remote Sensing Dynamic Monitoring on the Land Destruction and Recovery of Green Mines

The construction of green mine is the only way to promote ecological civilization and realize the harmonious development of man and nature. This paper introduces the methods and steps of using remote sensing technology to carry out dynamic monitoring on green mines, and summarizes the main monitoring elements of green mine environment, and analyse the status quo and dynamic changes of land destruction and recovery of green mines according to the monitoring results. On the whole, the land recovery rate of green mines is low in China, and the level of land recovery is uneven in different regions, mines and minerals. The result shows a tendency that the land occupation of green mines has changed from the extension of the land destruction in the early stage to the recovery of the stock mining land. The main recovery modes of green mine include prevention engineering, ecological protection engineering, functional restoration and reconstruction, land reclamation, soil restoration and so on. It is necessary to strengthen the application of radar data to monitor mining subsidence area and the application of hyperspectral data to enhance the monitoring of mine recovery effect.

Canopy Hyperspectral Reflectance of Redroot Pigweed versus Okra and Super Okra Leaf Cotton

Redroot pigweed (Amaranthus retroflexus L.) is a nuisance weed that affects cotton (Gossypium hirsutum L.) growth and yield worldwide. Being able to distinguish redroot pigweed from cotton would help producers and crop consultants better implement strategies used to suppress and control it. Hyperspectral reflectance properties of weed and crop canopies have been used to differentiate between them. Currently, no information is available on the application of hyperspectral data to distinguish redroot pigweed from cotton with different leaf shapes. Positive results will further support the exploration of remote sensing technology for distinguishing redroot pigweed from cotton. The objectives were to compare canopy hyperspectral reflectance of redroot pigweed to canopy hyperspectral reflectance of okra and super okra leaf cotton and to identify regions of the spectrum in which differences exist in their reflectance properties. Hyperspectral reflectance measurements of redroot pigweed and cotton were obtained with a spectroradimeter on May 6 and June 27, 2019. Plants grown in a greenhouse were used for this study. One-hundred and sixty-two 10-nm bands (400 - 2350 nm spectral range) were evaluated with analysis of variance (p ≤ 0.05) and Dunnett’s test (p ≤ 0.05) to determine the wavebands that were useful for separating redroot pigweed from okra leaf and super okra leaf cotton. The following bands were consistent in distinguishing redroot pigweed and okra leaf cotton on both dates: 420 nm, 510 - 650 nm, 690 - 740 nm, and 2000 - 2010 nm; whereas, 400 - 500 nm, 1480 - 1780 nm, and 1990 - 2350 nm were identified for both dates for separating redroot pigweed from super okra leaf cotton. Commercial imaging systems used on ground-based or airborne platforms can be easily tuned into the spectral bands listed in this study, thus providing managers with a tool to use for identifying redroot pigweed in cotton production systems.

CLASSIFICATION OF HYPERSPECTRAL DATA BASED ON GUIDED FILTERING AND RANDOM FOREST

Abstract. Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

A shadow identification method using vegetation indices derived from hyperspectral data

ABSTRACTShadows in high-spatial-resolution remote-sensing images become more pronounced. The detection of shadows is an essential requirement for both detailed high-spatial land-cover classification and applications such as three-dimensional (3D) reconstruction of buildings as well as cloud removal. This article presents a method for integrating the photochemical reflectance index (PRI) and Red Edge normalized difference vegetation index (RENDVI) for shadow identification (IPRSI) using high-spatial-resolution airborne hyperspectral data. This method detects shadows by setting thresholds to the PRI and RENDVI to separate shadows from vegetated and non-vegetated areas. The proposed method outperformed the invariant colour spaces model and the object-based method in terms of shadow extraction accuracy. The overall shadow identification accuracy of the IPRSI was 88.97% with an F-score of 90.96 (81.32% with F-score 81.97 for the invariant colour spaces model and 78.02% with F-score 82.07 for the object-based method). The IPRSI is a potential method with the wide application of hyperspectral data in high spatial resolution that is increasingly easier to be obtained with the development of remote-sensing platforms (such as unmanned aerial vehicles (UAVs), small satellites, and airships).

Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm

Phaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize. Field data were collected from healthy and the early stage of PLS over two years (2013 and 2014) using a handheld spectroradiometer. An integration of a newly developed guided regularized random forest (GRRF) and a traditional random forest (RF) was used for feature selection and classification, respectively. The 2013 dataset was used to train the model, while the 2014 dataset was used as independent test dataset. Results showed that there were statistically significant differences in biochemical concentration between the healthy leaves and leaves that were at an early stage of PLS infestation. The newly developed GRRF was able to reduce the high dimensionality of hyperspectral data by selecting key wavelengths with less autocorrelation. These wavelengths are located at 420 nm, 795 nm, 779 nm, 1543 nm, 1747 nm, and 1010 nm. Using these variables (n=6), a random forest classifier was able to discriminate between the healthy maize and maize at an early stage of PLS infestation with an overall accuracy of 88% and a kappa value of 0.75. Overall, our study showed potential application of hyperspectral data, GRRF feature selection, and RF classifiers in detecting the early stage of PLS infestation in tropical maize.

Estimating High-Resolution Urban Surface Temperature Using a Hyperspectral Thermal Mixing (HTM) Approach

Land surface temperature (LST) plays an important role in characterizing surface energy fluxes as well as climatology. High-spatial-resolution LST values are needed to profoundly understand urban climatology through identifying its detailed thermal distribution patterns. In this paper, a simple method that uses hyperspectral spectral mixture analysis (SMA) to estimate fine resolution urban LST is presented. The proposed approach takes account of the universal existence of thermally mixed pixels in complex urban areas and includes mainly two steps. In the first step, multiple endmember SMA (MESMA) is applied to calculate land composition fractions. With the retrieved component abundances and associated thermal responses, high-resolution urban LST is estimated through a linear combination of each thermal response component multiplied by its respective abundance in the second step. In particular, hyperspectral data were used to enhance the spatial information extraction through improved accuracy in retrieving land cover abundance. Evaluation of the proposed method was performed at four spatial scales (4, 8, 16, and 32 m) over two scenes with different land cover characteristics. Results show a relatively modest agreement between the estimated LST and the reference LST by both visual interpretation and quantitative accuracy metrics, with a root-mean-square error (RMSE) of 4.18 and 1.71 K as well as the mean absolute error (MAE) of 3.62 and 0.89 K for the two scenes separately. Overall, this approach enhances the application of hyperspectral data for estimating high-spatial-resolution LST; therefore, it can serve as a promising technique in urban applications.

AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

Abstract. In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

Current Status of Hyperspectral Data Processing Techniques for Monitoring Coastal Waters

In this study, we introduce various hyperspectral data processing techniques for the 2014년 10월 28일 접수 Received on October 28, 2014 / 2014년 12월 26일 수정 Revised on December 26, 2014 / 2015년 1월 26일 심사완료 Accepted on January 26, 2015 1 한국해양과학기술원 해양위성연구센터 Korea Ocean Satellite Research Center, Korea Institute of Ocean Science and Technology 2 한국해양대학교 해양과학기술전문대학원 OST-School, Korea Maritime and Ocean University 3 과학기술연합대학원대학교 해양환경시스템과학과 Dept. of Marine Environmental System Science, Korea University of Science and Technology(UST) ※ Corresponding Author E-mail : yangsc@kiost.ac 연안해역 모니터링을 위한 초분광영상 처리기법 현황 / 김선화· 양찬수 49 monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

The quantitative evaluation of application of hyperspectral data based on multi-parameters joint optimization

In order to evaluate the mineral identification of the hyperspectral data and make a trade-off of the imaging system parameters, a quantitative evaluation approach based on the multi-parameters joint optimization is proposed for the hyperspectral remote sensing. In the proposed approach, the mineral identification is defined as the number of the minerals identified and the key imaging parameters employed include ground sample distance (GSD) and spectral resolution (SR). Certain limitations are found among parameters that are used for analyzing the imaging processes. The constraints include the industrial manufacturing level, application requirements and the quantitative relationship among the GSD, the SR and the signal-to-noise ratio (SNR). Regression analysis is used to investigate the quantitative relationship between the mineral identification and the key imaging system parameters. Then, an optimization model for the trade-off study is established by combining the regression equation with the constraints. The airborne hyperspectral image collected by Hymap is applied to evaluate the performance of the proposed approach. The experimental results reveal that the approach can achieve the evaluation of the mineral identification and the trade-off of key imaging system parameters. The error of the prediction is within one kind of mineral.

An Evaluation of EO-1 Hyperion Data for Estimating Leaf Area Index of Rubber Plantations

The leaf area index (LAI) is one of the most important parameters for quantifying the physical conditions of rubber plantations. Modern remote sensing tools such as hyperspectral sensors can be effectively used for estimating the LAI of crops. Unfortunately, only few examples are found in the literature on the application of hyperspectral data for estimating rubber LAI and the understanding of the underlying mechanisms remain unclear. Thus, the aim of this study is to explore one step beyond the existing research. The current study is the first time that the capability of hyperspectral data for estimating the LAI of rubber plantations has been investigated. Four popular vegetation indices (i.e., Simple Ratio index (SR705), Modified Simple Ratio Index (MSR705), Normalized Difference Vegetation Index (NDVI705), and Modified Soil Adjusted Vegetation Index (MSAVI705)) and the EO-1 Hyperion image of the rubber plantations in Pak Chom District, Loei Province, Thailand were the chosen for the investigation. Despite additional fine-tuning need to be done on the statistical model parameters, the proposed models reveal significant high statistical correlations. The best-fitted model was determined to be the MSAVI705 model (R2 = 0.775), which possesses the lowest RMSE values (=0.160). It is anticipated that the methodology presented in this study can be used as a guideline for estimating the LAI of rubber plantations in other areas.