Hyperion Image Research Articles

Vertical stripe correction in Hyperion image using wavelet transformation and singular value decomposition (SVD)

Vertical striping are quite common in Hyperion images. The removal of these stripes turns out to be an important step of noise correction along with atmospheric and other artifact correction. Often these corrections are applied on radiometric values in spatial domain or fequency domain. We have proposed a novel approach to correct these vertical stripes using wavelet transformation and singular value decomposition. In the first step, we proposed stripe identification based on the Eigen ratio of first and second Eigen values of vertical components (decomposed using wavelet transformation). This ratio is supposed to be very high for striped images and minimal for stripe free images. This is because of the contribution to first Eigen value of high frequency stripes. These high frequency stripes contributes tremendously to the first Eigen because of their dominance in vertical component. We have also proposed a threshold function to identify the bands that needs to be corrected. After the stripes identification and correction algorithm is appllied on respective bands, it is observed that proposed method successfully removed striping noise with high accuracy. Upon comparing the results of proposed method with various other methods, it is found that proposed method have an edge and performing better than rest of the methods. To assess the results, SNR value was calculated for all methods and found highest for the proposed method except Local Threshold approach. Local Threshold have better SNR probably because of intact noise (even in visual inspection but also present in frequency plot of vertical wavelet component) in these methods.

Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets

In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.

Satellite imagery and spectral matching for improved estimation of calcium carbonate and iron oxide abundance in mine areas

This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in the iron ore mines of Noamundi, Eastern India. After noise removal in the Sentinel 2A (multispectral) and EO-1 Hyperion (hyperspectral) image datasets, spectral matching is performed using the Jeffries-Matusita (JM) distance, Spectral Correlation Mapper (SCM), and combined JM-SCM measure. Due to the specific absorption spectra for carbonates (1900 nm, 2000 nm, and 2160 nm) and iron oxide (865 nm), it is possible to identify and map such mineral deposits using the multispectral dataset (Sentinel 2A) and hyperspectral dataset (EO-1 Hyperion) respectively. The grade-wise mapping of carbonate in the Ariyalur mine using the Sentinel 2A dataset by the Jeffries-Matusita (JM) approach and Spectral Correlation Mapper (SCM) yielded R2 values of 0.44 and 0.77 respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.87. The grade-wise mapping of iron oxide in Noamundi using the EO-1 Hyperion dataset by the Jeffries-Matusita (JM) approach and the Spectral Correlation Mapper (SCM) approach yielded R2 values of 0.15 and 0.76, respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.90. Such an improved performance of the combined approach is primarily due to the simultaneous and effective utilization of band-wise information (by JM) and correlation aspects (by SCM) of the reference and target spectra considered in the matching algorithm. Thus, in this study, the proposed algorithm proved its compatibility and utility in extracting information on mineral abundance distribution for mine areas.

GENERIC LINEAR ARRAY SCANNER MODELING OF SPECTRAL IMAGING SYSTEMS CONTAINING LIMITED METADATA

Abstract. The National Geospatial-Intelligence Agency (NGA) designed the Generic Linear Array Scanner (GLAS) model for geopositioning images from both airborne and spaceborne linear array scanning systems, including pushbroom, whiskbroom, and panoramic sensors. Providers of hyperspectral imagery (HSI) historically have not populated products with high fidelity metadata to support downstream photogrammetric processing. To demonstrate recommended metadata population and exploitation using the GLAS model, NGA has generated example HSI products using data collected by NASA’s EO-1 Hyperion sensor and provided courtesy of the U.S. Geological Survey. This paper provides novel techniques for: 1) generating reasonably accurate initial approximations for GLAS metadata as a function of per-image metadata consisting of only timing information and the latitude and longitude values of the four corners of the image; and 2) identifying a vector of adjustable parameters and reasonable values for its a priori error covariance matrix that enable corrections to the metadata during a bundle adjustment. The paper describes applying these techniques to fourteen overlapping Hyperion images of the Alps, running a bundle adjustment as a function of tie points and optional ground control points, and demonstrating superior results to the previous polynomial based approach as quantified by the 3D errors at several ground check points.

A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing

Grassland degradation is an important research topic on a global scale, since it can severely restrict the development of animal husbandry and threaten ecological security. The proper monitoring of regional grassland degradation is the basis for strengthening grassland protection and restoration, as well as improving grassland ecology. In this study, the standards for monitoring grassland degradation at the regional level were established based on the field data measured in the study area and the data of a grazing-controlled experimental plot. We extracted the spectral characteristic parameters and carried out the spectral dimensionality reduction and accuracy evaluation using principal component analysis (PCA) and the multilayer perceptron neural network (MLPNN). Based on the EO-1 Hyperion images, multiple endmember spectral mixture analysis (MESMA) and the fully constrained least squares method pixel un-mixing (FCLS) were used to identify typical vegetation species and assess the degree of grassland degradation at the regional level per the established grassland degradation monitoring standards. This new method of monitoring grassland degradation from the perspective of the vegetation species composition not only makes grassland degradation monitoring more accurate, but also provides a reference for relevant studies.

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth’s surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement. The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson’s correlation test. The MLP and RBF networks were generated based on the OLI bands 1–7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands.

Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala

In this study we evaluated the applicability of a space-borne hyperspectral sensor, Hyperion, to resolve for chlorophyll a (Chl a) concentration in Lake Atitlan, a tropical mountain lake in Guatemala. In situ water quality samples of Chl a concentration were collected and correlated with water surface reflectance derived from Hyperion images, to develop a semi-empirical algorithm. Existing operational algorithms were tested and the continuous bands of Hyperion were evaluated in an iterative manner. A third order polynomial regression provided a good fit to model Chl a. The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chl a concentrations in Lake Atitlan during the dry season, with a relative error of 33%. This analysis confirmed the suitability of hyperspectral imagers like Hyperion, to model Chl a concentrations in Lake Atitlan. This study also highlights the need to test and update this algorithm with operational multispectral sensors such as Landsat and Sentinel-2.

Noise reduction and destriping using local spatial statistics and quadratic regression from Hyperion images

Hyperion images from Earth Observing-1 (EO-1) are being used in natural resources assessment and management. The evaluation and verification of Hyperion images for the above applications are validating the EO-1 mission. However, the presence of random and striping noises in Hyperion images affect the accuracy of the results. Therefore, reduction of random noise and stripes from Hyperion images becomes indispensable for the evaluation of the results in natural resources assessment and in optimum use of the data. Thus, a collective approach for correcting pixels with no-data values and removing random noise and stripes from Hyperion radiance images is developed. In the developed method, first, no-data valued pixels are identified and corrected using a local median filter. Minimum noise fraction transformation is then used to reduce random noise from noise-dominated bands. Further, spatial statistical techniques are used to reduce random noise from the rest of the bands. Finally, a local quadratic regression by a least squares method is used to correct bad columns and global stripes, and a local-spatial-statistics-based algorithm is used to detect and correct local stripes. The effectiveness and efficiency of the algorithm is demonstrated by application to two Hyperion images: one from the Udaipur area, western India, and another from the Lulea area, northern Sweden. The results show that the algorithm reduces random and striping noise without introducing unwanted effects and alterations in the original normal data values in the images.

CLASSIFIER FUSION OF POLSAR, HYPERSPECTRAL AND PAN REMOTE SENSING DATA FOR IMPROVING LAND USE CLASSIFICATION

Abstract. The combined use of PolSAR and hyperspectral data can improve the classification accuracy. This paper proposes a new classification approach for combining use of PolSAR and hyperspectral image data sets. At the first step, polarization signature is generated from coherency matrix of PolSAR image data. In the second step, in order to improve spatial resolution, the Hyperion image was pan-sharped with the ALI Pan image. In the third step, the Random Forest (RF) classifier is used for classifying PolSAR and hyperspectral data sets in five different classes including: Water (Wa), urban area (Ur), vegetation (Vg), road (Ro), and soil (So). Then, in order to fuse the output of RF for incorporated two data sets, simple majority voting (MV) and weighted majority voting (WMV) methods are used. Three UAVSAR, Hyperion and ALI images that acquired on April 2015 was chosen for this study. The results showed the ability of the polarimetric data for classifying urban and vegetation, and hyperspectral images for water, soil and road classes. Also, the combination of two data sets by using of WMV method causes the improvements of the classification performance.

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACTWe designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.

Investigating the response of soil and vegetable crops to poultry and cow manure using ground and satellite data.

Based on the massive production of cow and poultry manures, farmers in Saudi Arabia are moving towards the application of organic fertilizers in their farms. Therefore, the present work was conducted to study the response of soil and selected vegetable crops to poultry and cow manures, using ground data and Landsat-8 and Hyperion images. The studied vegetable crops are cabbage, cauliflower, broccoli, and lettuce. A total of 100 t ha−1 organic manures were applied as a pre-planting treatment. A 12.5 ha field in Tawdeehiya Farms, 200 km southeast of Riyadh, was earmarked for this study. The field was divided into sectors cultivated with the above-mentioned vegetable crops. Soil characteristics, including the soil pH, the electric conductivity (EC), the nitrogen (N), the phosphorus (P) and the potassium (K), were examined before the application of manures and 25 days after the transplanting process. Observations on crops chlorophyll content, number of leaves, the diameter of merchantable products and yield were also investigated. Furthermore, the relationship between the crop performance and yield was investigated through the satellite images generated vegetation indices (VIs). This study revealed the better performance of poultry manure compared to cow manure in terms of development and production parameters of the experimental crops. Dynamics of the chlorophyll content across the crop growth period revealed that all the tested crops responded significantly (R2 = 0.69; P = 0.001) to the poultry manure treatments. Among the tested crops, the chlorophyll content, curd or head sizes and crop yields were quite better in poultry manure applied plots. The investigation of crop yield was significant with poultry manure (R2 = 0.64; P = 0.001) than cow manure (R2 = 0.57; P = 0.001) using the OSAVI and mNDVI, respectively.

Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas

The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands data) and Landsat (Broad-bands data) images, with the aid of eight different vegetation indices (VIs) and field-based laboratory spectra. A PCE Instrument for measurement of dust accumulation on leaves and Spectroradiometer for spectral signatures, was also used to estimate foliar dust concentration. The result depicted a negative relationship between VIs (Hyperion and Landsat satellite imagery), and field based dust measurements. The Normalized Difference Vegetation Index (NDVI) shows an excellent negative correlation (R2= 0.89 for Hyperion and R2 = 0.81 for Landsat) as it is not much affected by the variation in vegetation types and patterns. Amongst the eight VIs, NDVI was selected as an optimal VI (RMSE = 0.06 g/m2for Hyperion and 0.11 g/m2for Landsat) based on both, the field measurement and satellite data for estimation of foliar dust concentration. Furthermore, a positive relationship between the field-based measured dust concentration (g/m2) and satellite image (by VIs) based dust concentration (g/m2) was observed. Field-based measured foliar dust concentration taken for 20 samples was plotted against their estimated dust values using the NDVI (R = 0.90 for Hyperion and R = 0.81 for Landsat). Hyperion data is considered as the reliable one as it gave better results than the Landsat data. Finally, the Hyperion data based foliar dust map was analyzed by a High-resolution Google Earth image (Geo Eye) for different locations viz., mines, transport sites as well as forests and matched with the field-based measured dust concentration. The result shows that maximum foliar dust was concentrated near the ore transportation network, surrounding mining locations, tailing ponds, and mining dumps areas. For making the environmental management effective (in the mining and allied areas), Hyperspectral remote sensing aided by field-based methods, for estimating foliar dust concentration would be very helpful.

Assessment of the application of copper stress vegetation index on Hyperion image in Dexing Copper Mine, China

We are the first to apply the copper stress vegetation index (CSVI) on a remotely sensed image and the purpose is to verify the effectiveness of CSVI at the satellite-image scale. The study area was located at the Dexing Copper Mine, Jiangxi Province, China. First, the data preprocessing for the Hyperion image was conducted, including bands removal, radiometric calibration, and atmospheric correction. Second, the regions with high vegetation cover were extracted based on endmembers extraction and spectral unmixing. The CSVI was calculated on the high-vegetation-cover regions. Third, the samples of soil and leaves were collected from the study area and the copper contents in the samples were measured for the assessment. The results showed that the high values of the CSVI were near the functional regions for copper mining and the polluted rivers. What is more, there was a significant positive correlation between the CSVI calculated from the Hyperion image and the copper content in soil/leaves. We demonstrate that CSVI is applicable for monitoring the copper stress on vegetation using satellite hyperspectral images. In addition, we provide a complete example for the application of CSVI at the satellite-image scale for the first time, which is helpful for the community in remote sensing of copper-stressed vegetation.

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGERY WITH REGRESSION KRIGING AND THE LULU OPERATORS; A COMPARISON

Abstract. In this digital world, there is a large requirement of high resolution satellite image. Images at a low resolution may contain relevant information that has to be integrated with the high resolution image to obtain the required information. This is being fulfilled by image fusion. Image fusion is merging of different resolution images into a single image. The output image contains more information, as the information is integrated from both the images Image fusion was conducted with two different algorithms: regression kriging and the LULU operators. First, regression Kriging estimates the value of a dependent variable at unsampled location with the help of auxiliary variables. Here we used regression Kriging with the Hyperion image band as the response variables and the LISS III image bands are the explanatory variables. The fused image thus has the spectral variables from Hyperion image and the spatial variables from the LISS III image. Second, the LULU operator is an image processing methods that can be used as well in image fusion technique. Here we explored to fuse the Hyperion and LISS III image. The LULU operators work in three stages of the process, viz the decomposition stage, the fusion and the reconstruction stage. Quality aspects of the fused image for both techniques have been compared for spectral quality (correlation) and spatial quality (entropy). The study concludes that the quality of the fused image obtained with regression kriging is better than that obtained with the LULU operator.

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set’s reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.

A Sub-Pixel Multiple Change Detection Approach for Hyperspectral Imagery

One of the most important applications of remote sensing is change detection (CD). The accurate detection of changes is of great significance for the optimal management of available resources. This article presents an unsupervised ‘multiple-change detection’ method using multi-temporal hyperspectral imaging based on the integration of an unmixing technique, multi-resolution segmentation, similarity measure methods, and the Otsu algorithm. The proposed method is presented in the context of two main scenarios: the first scenario is hyperspectral change detection (HSCD) at the sub-pixel level with no ancillary data; and the second is the HSCD at the sub-pixel level based on ancillary data (high-resolution panchromatic (PAN) data). The main advantages of the proposed method are that it is unsupervised, easy to use, providing ‘multiple-change’ maps at the sub-pixel level with high accuracy. To evaluate the performance of the proposed method, real bi-temporal hyperspectral Hyperion images and high spatial resolution of PAN data belonging to the Advanced Land Imager (ALI) sensor with a variety of land cover classes were used. The results show the overall accuracy improved by >5%, and kappa coefficient improved by >0.13 with respect to the results obtained from their original resolution.

Estimation of water stress variability for a rice agriculture system from space-borne hyperion imagery

Leaf relative water content (LRWC) for paddy crop is a key component in evaluating crop growth. Present study aims to assess the variability of water stress in a rice agriculture system for summer/rabi and winter/kharif rice during pre-monsoon and monsoon period respectively. Amongst the examined indices, water band index (WBI), normalized water index-3 (NWI-3), normalized water index-4 (NWI-4) are considered to indicate water stress in paddy crop based on statistical performance of ground based hyperspectral data. Regression models are developed between each index and LRWC of paddy crop at three phases - specifically the vegetative, reproductive and ripening phases of paddy crop. The result showed that the reproductive phase of paddy crop is the critical growth phase for the aforementioned indices which followed different linear and nonlinear relationships. Moreover, mapping of LRWC of rice agriculture system from hyperspectral imagery is achieved by employing the best index models such as WBI, NWI-3 and NWI-4. The best estimates of LRWC at the reproductive phase for pre-monsoon (summer) rice agriculture system exhibited wide variation from 61.29 to 91.23% (WBI), 62.01 to 87.49 %(NWI-3) and 54.95 to 89.14% (NWI-4). On the other hand, the best VNIR index models (WBI, NWI-3 and NWI-4) that well predicted the LRWC of paddy crop varied from 60.21 to 90.45% (WBI), 58.58 to 85.67% (NWI-3) and 49.08 to 90.40% (NWI-4) for winter rice agriculture system. It is interesting to note that LRWC mapped from Hyperion image has a good agreement with the observed in-situ LRWC value (60.64–91.82%) obtained for the rice varieties in an Indian rice agriculture system. This study will provide essential information to assess the paddy field heterogeneity in a rice agriculture system.

Evaluation of the Use of Hyperspectral Vegetation Indices for Estimating Mangrove Leaf Area Index in Middle Andaman Island, India

ABSTRACTMangroves are highly productive ecosystems of the coastal environment in tropics and subtropics. The Leaf Area Index (LAI) is a key structural variable of the canopy, useful for estimating the primary productivity of vegetation including mangroves. We evaluated the sensitivity of narrowband spectral indices viz., Simple Ratio, Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index and Non-Linear Vegetation Index to mangrove LAI, and thereby propose the optimal bands and spectral indices for mangrove LAI estimation. The study was carried out using EO (Earth Observation)-1 Hyperion data in the Middle Andaman Island, situated in the Bay of Bengal, India. We constructed simple, normalized and non-linear band indices from all possible band pairs in the Hyperion image, and correlated them with mangrove LAI, and empirical relationships were validated through a k-fold cross-validation. The 2-D correlation plot analysis indicates that wavebands in Shortwave Infra-Red and Near Infra-Red regions, have a high potential for mangrove LAI estimation. The wavebands at 834 nm, 844 nm, 864 nm, 1034 nm, 1185 nm, 1276 nm, 1296 nm, 1629 nm and 1679 nm were the most sensitive to mangrove LAI. The non-linear vegetation indices performed better than linear indices in predicting LAI of mangrove. The identified indices are expected to improve quantification of spatial variability of mangrove LAI.