Optimal band selection from hyperspectral data for Lantana camara discrimination

The primary objective of this research was to determine the optimal hyperspectral wavelengths based on spectroscopy data over the spectral range of 450–2500 nm for the detection of the invasive species Lantana camara L. (lantana) from seven of its co-occurring species. A procedure based on statistical analysis of the reflectance and the first derivative reflectance (FDR) identified 86 and 18 bands, respectively, where lantana significantly differed from its co-occurring species. The effectiveness of the identified optimal bands was then evaluated using Hyperion imagery. The original Hyperion image with 155 bands gave an overall accuracy of 80% compared to 77% and 76% from the 86- and 18-band spectral subsets, respectively. A pairwise comparison of the three error matrices showed no significant difference in the accuracy achieved. The FDR analysis combined with the statistical analysis proved to be a useful procedure for data reduction by refining the discrimination to fewer optimal bands for lantana detection with no adverse impact on classification accuracy.     

Spectral characteristics and feature selection of hyperspectral remote sensing data

Hyperspectral remote sensing data with bandwidth of nanometre (nm) level have tens or even several hundreds of channels and contain abundant spectral information. Different channels have their own properties and show the spectral characteristics of various objects in image. Rational feature selection from the varieties of channels is very important for effective analysis and information extraction of hyperspectral data. This paper, taking Shunyi region of Beijing as a study area, comprehensively analysed the spectral characteristics of hyperspectral data. On the basis of analysing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from hyperspectral remote sensing data was proposed.     

Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.     

Ronda peridotite massif: Methodology for its geological mapping and lithological discrimination from airborne hyperspectral data

The Ronda peridotite massif in the Sierra Bermeja, southern Spain, was imaged in July 1991 by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) during the Europe 1991 Multispectral Airborne Campaign. Principal component analysis was used (i) to extract the most spectrally extreme pixels for empirical line calibration; (ii) to physically interpret the image spectral information; and (iii) to define an optimal endmember selection based on both spatial and spectroscopic characteristics. Two successive spectral mixture analyses that allow one to focus on subtle spectral variations related to bedrock and soil lithology were applied. The first spectral mixture analysis was used to identify the major surface constituents in the image and extract the geological target to be investigated, i.e. the peridotite massif; and the second one was used to model the spectral variability within the designated target. Although mineralogical variations observed in the rocks were at a sub-pixel scale for the airborne survey, spatially organized units could be identified within the major outcrops of the peridotite massif from their spectral variations. A mineralogical interpretation of these spectral variations is proposed in relation with the field observations, in terms of relative abundance variations in the pyroxene/olivine ratio among the pixels. This work demonstrates that the proposed methodology makes possible the spectral distinction of lithological units within an ultramafic body, despite the occurrence of partial vegetation cover and multiple sub-pixel mixtures. Furthermore, it shows that hyperspectral data can provide such information, resulting in a very cost-effective method of petrologic mapping.     

Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.     

Spectral reflectance characterization of shallow lakes from the Brazilian Pantanal wetlands with field and airborne hyperspectral data

The relationships between field water reflectance spectra and physico-chemical data of seven freshwater and five saltwater lakes from the Brazilian Pantanal wetlands were characterized. Selection of the lakes was based on previous inspection of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Principal component analysis (PCA) was used to identify homogeneous groups of lakes, in which the regression relationships were evaluated. The continuum removal method was applied to characterize minor spectral variations in the depth of the absorption bands present in field and image spectra. The results showed lakes with very distinct spectral characteristics. The transition from the freshwater to the saltwater lakes was characterized by lower values of depth and Secchi depth, larger concentrations of dissolved organic carbon (DOC), total suspended sediments (TSS), calcium (Ca), magnesium (Mg), sodium (Na) and potassium (K), and higher values of pH and electrical conductivity. The saline lakes presented a higher overall reflectance in the 400–900?nm range than the freshwater lakes, as indicated by the first principal component. From the optically active constituents analysed, DOC better explained variations in water reflectance. The discrimination of the saltwater lakes along the second principal component was due to the decrease in the chlorophyll (Chl) and to the increase in the DOC concentrations from the greenish to the bluish saline lakes. The AVIRIS instrument was able to detect the narrow 630?nm absorption band present in field water reflectance spectra.     

Spectral unmixing of airborne hyperspectral data for baseline mapping of mine tailings areas

The Kam Kotia mine tailings areas near Timmins in Ontario, Canada have been generating and discharging acidic mine drainage (AMD) into the surrounding areas for more than 35 years, killing large areas of forest and polluting the local water system. This paper presents results from the remote sensing monitoring programme in the Kam Kotia mine. Hyperspectral TRW (Thompson Ramo Wooldridge Inc.) Imaging Spectrometer III data were acquired over the Kam Kotia mine and tailings areas. This paper describes (1) the data pre‐processing (noise removal, atmospheric correction, spectral smile correction, scene‐based calibration) needed to radiometrically calibrate the images and (2) a novel procedure which combines constrained spectral mixture analysis and threshold‐based classification. With this developed procedure one can retrieve fraction maps of major mine tailings‐related surface materials and hence generate a surface map separating green vegetation, transition zones, dead vegetation, and oxidized tailings, and calculate the extent (surficial area) of each of the zones. The four zones are correlated with the extent and degree of vegetation cover affected by tailings material and are interpreted to span respectively from very low to medium, high, and very high AMD pollution. This procedure can be used to monitor changes in the course of the boundary between affected zones and finally quantify the rehabilitation process in mine tailings areas with high vegetation cover.     

Detection of stressed oil palms from an airborne sensor using optimized spectral indices

Existing vegetation indices and red-edge techniques have been widely used for the assessment of vegetation status and vegetation health from remote-sensing instruments. This study proposed and applied optimized Airborne Imaging Spectrometer for Applications (AISA) airborne hyperspectral indices in assessing and mapping stressed oil palm trees. Six vegetation indices, four red-edge techniques, a standard supervised classifier and three optimized AISA spectral indices were compared in mapping diseased oil palms using AISA airborne hyperspectral imagery. The optimized AISA spectral indices algorithms used newly defined reflectance values at wavelength locations of 734 nm (near-infrared (NIR)) and 616 nm (red). The selection of these two bands was based on laboratory statistical analysis using field spectroradiometer reflectance data. These two bands were then applied to the AISA airborne hyperspectral imagery using the three optimized algorithms for AISA data. The newly formulated AISA hyperspectral indices were D2 = R ₆₁₆/R ₇₃₄, normalized difference vegetation index a (NDVIa)?=?(R ₇₃₄ – R ₆₁₆)/(R ₇₃₄?+?R ₆₁₆) and transformed vegetation index a (TVIa)?=?((NDVIa?+?0.5)/(abs (NDVIa?+?0.5))?×?[abs (NDVIa?+?0.5)]^1/2. The classification results from the optimized AISA hyperspectral indices were compared with the other techniques and the optimized AISA spectral indices obtained the highest overall accuracy. D2 and NDVIa obtained 86% of overall accuracy followed by TVIa with 84% of overall accuracy.     

Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data

Discrete wavelet analysis was assessed for its utility in aiding discrimination of three pine species (Pinus spp.) using airborne hyperspectral data (AVIRIS). Two different sets of Haar wavelet features were compared to each other and to calibrated radiance, as follows: (1) all combinations of detail and final level approximation coefficients and (2) wavelet energy features rather than individual coefficients. We applied stepwise discriminant techniques to reduce data dimensionality, followed by discriminant techniques to determine separability. Leave-one-out cross validation was used to measure the classification accuracy. The most accurate (74.2%) classification used all combinations of detail and approximation coefficients, followed by the original radiance (66.7%) and wavelet energy features (55.1%). These results indicate that application of the discrete wavelet transform can improve species discrimination within the Pinus genus.     

Correcting brightness gradients in hyperspectral data from urban areas

The analysis of airborne hyperspectral data is often affected by brightness gradients that are caused by directional surface reflectance. For line scanners these gradients occur in across-track direction and depend on the sensor's view-angle. They are greatest whenever the flight path is perpendicular to the sun-target-observer plane. A common way to correct these gradients is to normalize the reflectance factors to nadir view. This is especially complicated for data from spatially and spectrally heterogeneous urban areas and requires surface type specific models. This paper presents a class-wise empirical approach that is adapted to meet the needs of such images.Within this class-wise approach, empirical models are fit to the brightness gradients of spectrally pure pixels from classes after a spectral angle mapping (SAM). Compensation factors resulting from these models are then assigned to all pixels of the image, both in a discrete manner according the SAM and in a weighted manner based on information from the SAM rule images. The latter scheme is designed in consideration of the great number of mixed pixels.The method is tested on data from the Hyperspectral Mapper (HyMap) that was acquired over Berlin, Germany. It proves superior to a common global approach based on a thorough assessment using a second HyMap image as reference. The weighted assignment of compensation factors is adequate for the correction of areas that are characterized by mixed pixels.A remainder of the original brightness gradient cannot be found in the corrected image, which can then be used for any subsequent qualitative and quantitative analyses. Thus, the proposed method enables the comparison and composition of airborne data sets with similar recording conditions and does not require additional field or laboratory measurements.     

A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data

In order to retrieve bathymetry, substratum type and the concentrations of the optically active constituents of the water column, an integrated physics based mapping approach was applied to airborne hyperspectral data of Moreton Bay, Australia. The remotely sensed data were sub-optimal due to high and mid-level cloud covers. Critical to the correct interpretation of the resultant coastal bathymetry map was the development of a quality control procedure based on additional outputs of the integrated physics based mapping approach and the characteristics of the instrument. These two outputs were: an optical closure term which defines differences between the image and model based remote sensing signal; and an estimate of the relative contribution of the substratum signal to the remote sensing signal. This quality control procedure was able to identify those pixels with a reliable retrieval of depth and to detect thin and thick clouds and their shadows, which were subsequently masked out from further analysis. The derived coastal bathymetry in depths ranging 4-13 m for the mapped area was within ± 15% of boat-based multi-beam acoustic mapping survey of the same area. The agreement between the imaging spectrometry and the acoustic datasets varies as a function of the contribution of the bottom visibility to the remote sensing signal. As expected, there was greater agreement in shallower clear water (± 0.67 m) than quasi-optically deep water (± 1.35 m). The quantitative identification and screening of the optically deep waters and the quasi-optically deep waters led to improved precision in the depth retrieval. These results suggest that the physics based mapping approach adopted in this study performs well for retrieving water column depths in coastal waters in water depths ranging 4-13 m for the area and conditions studied, even with sub-optimal imagery.     

Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1

The seasonal characterization and discrimination of savannahs in Brazil are still challenging due to the high spatial variability of the vegetation cover and the spectral similarity between some physiognomies. As a preparatory study for future hyperspectral missions that will operate with large swath width and better signal-to-noise ratio than the current orbital sensors, we evaluated six Hyperion images acquired over the Estação Ecológica de Águas Emendadas, a protected area in central Brazil. We studied the seasonal variations in spectral response of the savannah physiognomies and tested their discrimination in the rainy and dry seasons using distinct sets of hyperspectral metrics. Floristic and structural attributes were inventoried in the field. We considered three sets of metrics in the data analysis: the reflectance of 146 Hyperion bands, 22 narrowband vegetation indices (VIs), and 24 absorption band parameters. The VIs were selected to represent vegetation structure, biochemistry, and physiology. The depth, area, width, and asymmetry of the major absorption bands centred at 680 nm (chlorophyll), 980, and 1200 nm (leaf water) and 1700, 2100, and 2300 nm (lignin-cellulose) were calculated on a per-pixel basis using the continuum removal method. Using feature selection and multiple discriminant analysis (MDA), we tested the discriminatory capability of these metrics and of their combined use for vegetation discrimination in the rainy and dry seasons. The results showed that the spectral modifications with seasonality were stronger with the savannah woodland-grassland gradient represented by decreasing tree height, basal area, tree density and biomass and by increasing canopy openness. We observed a reflectance increase in the red, red edge, and shortwave (SWIR) intervals towards the dry season. In the near-infrared, the reflectance differences between the physiognomies were smaller in the dry season than in the rainy season. From the 22 VIs, the visible atmospherically resistant index (VARI), visible green index (VIg), and normalized difference infrared index (NDII) were the most sensitive indices to water stress and vegetation cover, presenting the largest rates of changes between the rainy (March) and dry (August) seasons in shrub and grassland areas. Absorption band parameters associated with the lignin-cellulose spectral features in the SWIR increased towards the dry season with great amounts of non-photosynthetic vegetation (NPV) in the herbaceous stratum. The opposite was observed for the 680 nm chlorophyll absorption band and the 980 and 1200 nm leaf water features. In general, the number of selected metrics necessary for vegetation discrimination was lower in the dry season than in the rainy season. The best MDA-classification accuracy was obtained in the dry season using nine VIs (79.5%). The combination of different hyperspectral metrics increased the classification accuracy to 81.4% in the rainy season and to 84.2% in the dry season. This combination added a gain higher than 10% for the classification of shrub savannah, open woodland savannah and wooded savannah.     

A shadow identification method using vegetation indices derived from hyperspectral data

Shadows 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).     

Simulation of atmospheric profile retrieval from hyperspectral infrared data under cloudy conditions

In this paper, simulated space-based high spectral resolution AIRS (Atmospheric Infrared Sounder) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate measurement sensitivity and atmospheric profile retrieval performance. Simulated cloudy retrievals of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analysed with different effective cloud fractions and different cloud heights. The results show that knowledge of cloud height is critical to sounding retrieval performance and the root mean square error of retrieved temperature and the mixed ratio of water vapour below the cloud top increases with effective cloud fraction. When there is 50 hPa error in the cloud height the retrieval accuracy of temperature and humidity decrease, compared with when the cloud height is known perfectly; the temperature retrieval is more sensitive to cloud height error than humidity retrieval. Collocated cloudy AIRS and the associated clear MODIS (Moderate Resolution Imaging Spectroradiometer) infrared observations within the AIRS field of view (FOV) are also used to demonstrate profile retrieval improvement below the cloud layer. It is demonstrated that using collocated clear MODIS multispectral imager data along with AIRS high spectral resolution infrared radiances can greatly improve the single FOV cloudy retrieval even under opaque cloudy conditions.     

Geomorphologic mapping from hyperspectral data,using Gaussian mixtures and lower confidence bounds

Probabilistic classification under the Gaussian mixture model is normally based on posterior probability (p.p.) estimates of class membership. The question, how accurate they are for a given pixel, is traditionally left without attention, which may lead to unreasonable optimism about the classification results obtained. Addressing the issue, Koltunov and Ben‐Dor have proposed an unsupervised, lower confidence bound (l.c.b.)‐based method for thematic interpretation of remote sensing data. This method predicts the sampling properties of the p.p. estimators of a given pixel, to assess reliability of the estimates. The present paper describes a modified version of the method. In particular, instead of defining the l.c. bounds in terms of two first moments of the sampling distribution, as has been suggested previously, we use percentiles. Combining this with a probabilistic model of supervised identification of the mixture components yields the post‐classification uncertainty value for a given pixel and the confidence level, at which this value is proven to be maximal. In the application to an arid landscape in the Southern Negev desert, Israel, the compressed raw hyperspectral data acquired by the Digital Airborne Imaging Spectrometer (DIAS‐7915) was clustered once, whereas two thematic tasks were solved corresponding to different map legends, identification procedures, and the associated requirements to the level of detail and reliability of the thematic maps. The reference data collected in the field have provided evidence for accurate algorithmically estimated confidence bounds of the classification quality. The classification has revealed new information about the geomorphological subunits forming the study area.     

Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination

Due to the very large number of bands in hyperspectral imagery, two major problems which arise during classification are the ‘curse of dimensionality’ and computational complexity. To overcome these, dimensionality reduction is an important task for hyperspectral image analysis. An unsupervised band elimination method is proposed which iteratively eliminates one band from the pair of most correlated neighbouring bands depending on the discriminating capability of the bands. Correlation between neighbouring bands is calculated over partitioned band images. Capacitory discrimination is used to measure the discrimination capability of a band image. Finally, four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the selected bands to measure the efficiency of the proposed method. The proposed unsupervised band elimination technique is compared to three popular state-of-the-art approaches, both qualitatively and quantitatively, and shows promising results compared to them.     

Discrimination of lianas and trees with leaf-level hyperspectral data

Lianas are an important component of the biological diversity in two tropical forests with contrasting moisture regimes in Panama. However, their presence in a tree crown may be a source of confusion in remotely sensed data collected for inventories or assessment of vegetation health. The structural growth form of lianas contrasts with trees in that their proportion of leafy biomass to woody biomass is much higher. In effect, they use trees for structural support and typically form a monolayer of leaves above the crown of the supporting tree. Here, we investigated possible differences between hyperspectral signatures of lianas and trees at the leaf level using pattern recognition techniques. Our method involves principal components analysis followed by training and classification using a selection of supervised parametric and nonparametric classifiers. At a tropical dry forest site (Parque Natural Metropolitano), lianas and trees are distinguishable as groups based on their leaf spectral reflectance characteristics in dry season conditions. Classification was improved using ancillary data on leaf chlorophyll content. Their distinction at this site may be related to drought stress and/or phenological differences between the two groups. At a tropical wet forest site (Fort Sherman), discrimination between the two groups was not as clear. Additional research is required to determine the physiological basis of possible differences as well as to determine if these differences are observable at the canopy level.     

A comprehensive review of hyperspectral data fusion with lidar and sar data

With the development of remote sensing techniques, the fusion of multimodal data, particularly hyperspectral-Light Detection And Ranging (HS-LiDAR) and hyperspectral-SAR, has become an important research field in numerous application areas. Multispectral, HS, LiDAR, and Synthetic Aperture Radar (SAR) images contain detailed information about the monitored surface that are complementary to each other. Thus, data fusion methods have become a promising solution to obtain high spatial resolution remote-sensing images. The main point of this review paper is to classify hyperspectral-LiDAR and hyperspectral-SAR data fusion with approaches. Moreover, recent achievements in the fusion of hyperspectral-LiDAR and hyperspectral-SAR data are highlighted in terms of faced challenges and applications. Most frequently used data fusion datasets that include IEEE GRSS Data Fusion Contests are also described.     

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth.In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R², absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R², absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.     

高光谱遥感数据光谱特征提取算法与分类研究

针对高光谱数据的特点,探讨了高光谱数据特征提取的若干算法,重点研究了导数光谱和光谱编码技术,并从地物光谱曲线中提取了其光谱吸收特征.对同类曲线特征求交得到识别地物的有效特征;对不同类曲线特征求交得到区分不同类地物的有效特征.最后基于提取的特征建立了地物识别决策树,从而达到快速识别分类地物的目的,能够实现依据地物光谱特征的地物识别与分类.