Hyperion Datasets Research Articles

A statistical technique for noise identification and restoration of hyperspectral image cube

Hyperion, onboard EO-1 satellite is a hyperspectral sensor that scans the earth in 242 contiguous bands with 10 nm spectral resolution. The data has numerous applications in geology, agriculture, water resources, and many other fields. However, due to the miscalibration of detectors, the Hyperion dataset suffers from a few inherent errors like bad bands, bad columns, stripes, etc. which degrade the quality of the image. Thus, detection of these errors and their correction is an essential step before any scientific interpretation. The available literature suggests manual and semi-automatic techniques for error removal, which are time-consuming, complex, and not efficient. In the present study, a statistical technique was developed to detect the errors and correct the stripping artifacts, which is simple, less time-consuming, and fully automatic, thereby reducing processing time and human errors which might be induced if manually processed.

USING DESIS AND EO-1 HYPERION REFLECTANCE TIME SERIES FOR THE ASSESSMENT OF VEGETATION TRAITS AND GROSS PRIMARY PRODUCTION (GPP)

Abstract. This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400–2400 nm) reflectance collection over vegetated flux sites, to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using together VNIR reflectance from EO-1 Hyperion and DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.

Snow and glacial feature identification using Hyperion dataset and machine learning algorithms

Snow and glacial feature identification using Hyperion dataset and machine learning algorithms

An efficient lossless compression technique for remote sensing images using segmentation based band reordering heuristics

ABSTRACT The size of remote sensing (RS) image is indeed massive due to hundreds capturing wavelengths bands used for collecting information about the ground surface. The data in the RS images are often redundant spatially, spectrally and temporally which thus give a sufficient prospect for compressing the images in different domains. In this paper, we propose a band reordering scheme to be applied on the segmented subgroup datasets of an original RS image dataset towards presenting a hybrid lossless compression technique. The proposed dataset segmentation-based band reordering process employs two heuristics, i.e. consecutive continuity limit of previous bands and continuity breakdown thresholds to be used for efficient coding of reordered dataset towards attaining better on-board compression. The aim of the proposed compression strategy is thus to increase compression gain through reordering each highly correlated bands’ segment in the RS data-cube for efficient coding. The performance of the proposed band reordering method is measured using the Consultative Committee for Space Data Systems (CCSDS) recommended lossless CCSDS 123.0-B-1 and three-dimensional (3D) Context-based Adaptive Lossless Image Codec (CALIC) predictors. The experimental results illustrate substantially higher compression of the proposed method than that of the state-of-the-art standards using various real RS hyperspectral and hyperspectral sound datasets. Quantitatively, the compression performances are increased by 0.10%–0.66% using CCSDS and 0.65–2.52% using 3D-CALIC for hyperspectral Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) datasets, 0.26%–1.00% using CCSDS and 2.16–2.57% using 3D-CALIC for hyperspectral Hyperion datasets and 4.61%–7.36% using CCSDS and 4.05%–4.88% using 3D-CALIC for hyperspectral sounder datasets by the proposed compression technique. Besides, the proposed compression scheme outperforms state-of-the-art investigated compression methods.

Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data

Application of remote sensing makes the assessment and monitoring of mangroves both time and cost-effective. In this study, the capacity of AVIRIS-NG data in discriminating different mangrove species of Lothian Island of Indian Sundarbans has been evaluated and compared with hyperspectral (Hyperion) and multispectral dataset (Landsat 8 OLI and Sentinel-2). Spectral signatures of mangrove species were retrieved, and spectral libraries were created. With the corrected images and spectral libraries, mangroves were classified using appropriate classification techniques. For multispectral datasets (Landsat 8 OLI and Sentinel-2) and hyperspectral coarser-resolution Hyperion datasets, K-means classification followed by knowledge-based classification was adopted. For fine resolution hyperspectral AVIRIS-NG dataset, classification was accomplished using Support Vector Machine (SVM). The overall accuracy for the classification is significantly high in case of AVIRIS-NG data (87.61%) compared to the Landsat 8 OLI (76.42%), Sentinel-2 (79.81%), and Hyperion data (81.98%). The results showed that AVIRIS-NG hyperspectral dataset has the potential to classify not only the genus level but also species-level with satisfactory accuracy in a complex mangrove forest.

CCSDS-MHC on Raspberry Pi for Lossless Hyperspectral Image Compression

This paper is about Consultative Committee for Space Data System for Lossless Multispectral and Hyperspectral Image Compression (CCSDS-MHC) algorithm that is implemented on Raspberry Pi 3 Model B+ system using Open Multi-Processing (OpenMP). CCSDS-MHC algorithm along with the full prediction mode is opted due to its best compression ratio (CR) performance. The issue of Hyperspectral Image Compression is the loss of data when compress, thus CCSDS algorithm is used in this research. Besides, with current technologies that require low-power but high-performance devices, Raspberry Pi was chosen to be tested in terms of its performances while being compared to other platforms. AVIRIS (airborne) and Hyperion (spaceborne) are used to test the performance of the system. OpenMP is introduced to simplify the computational operation through parallelization to take advantage of the multi-core architecture of the hardware system. In term of execution time, CCSDS-MHC algorithm when parallelize using OpenMP gave the best performances about 69.4% for AVIRIS 1997 dataset, 69.3% for AVIRIS 2006 dataset and 67.7% for Hyperion dataset. The execution time of performance CCSDS-MHC in Raspberry Pi 3 Model B+ comparing with other different multicore platform is validate and the mean parallelize is measured. The result of comparison proves that faster performing task gives the best result due to higher speed of CPU-cores performances. From this research, it is proven that Raspberry Pi which is a low-power embedded platform able to compress the hyperspectral images at optimized speed with the implementation of OpenMP through CCSDS algorithm. Therefore, this research will be useful for further studies in lossless of hyperspectral images.

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.

Hyperspectral image quality evaluation using generalized regression neural network

In order to alleviate the overfitting problem caused by image quality evaluation (IQA) model learning under intolerably small dataset, this paper proposes a multi-feature fusion-based deep architecture for hyperspectral image quality assessment. First, eight key IQA-related features, which are descriptive to the mean noise of multi-band images, spatial correlation, inter-spectral correlation, blur, and the phase-consistent map of images, are extracted from each hyperspectral image within the dataset. Based on this, a carefully-designed generalized regression neural network (GRNN) with a limited number of parameters is hierarchically trained by the feature vectors from samples in the training IQA data set. Comprehensive experimental evaluations on the hyperspectral IQA images from the DOTA dataset and the EO-1 Hyperion dataset have shown that the proposed model can indicate the subjective/objective quality-aware images regions precisely In addition, we observe that our designed IQA method has received impressive IQA performance than the other state-of-the-art non-reference methods.

Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification

Information on the spatial and temporal extent of snow cover distribution is a significant input in hydrological processes and climate models. Although hyperspectral remote sensing provides significant opportunities in the assessment of land cover, the applications of such data are limited in the snow-covered alpine regions. A major issue with hyperspectral data is the larger dimensionality. Feature selection methods are often used to derive the most informative subset of bands from the hyperspectral data. In this study, a band selection technique is proposed which utilizes the mutual information (MI) between hyperspectral bands and a reference band. The first principal component of the hyperspectral data is selected as the reference band. Two variants of this approach are proposed involving preclustering of bands using: (1) the k-means and (2) the fuzzy k-means algorithms. The MI is derived from weighted entropy of the hyperspectral band and the reference band. The weights are computed from the cluster distance ratio and the cluster membership function for the k-means and fuzzy k-means algorithm, respectively. The selected bands were classified using random forest classifier. The proposed methods are evaluated with four datasets, two Hyperion datasets corresponding to the geographical locations of Dhundi and Solang in India, corresponding to snow covered terrain and two benchmark AVIRIS datasets of Indian Pines and Salinas. The average classification accuracy (0.995 and 0.721 for Dhundi and Solang datasets, respectively) for the proposed approach were observed to be better as compared with those from other state of the art techniques.

Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm

Detailed hydrothermally altered mineral mapping is important for mineral exploration. ASTER data are commonly combined with Hyperion data to classify the hydrothermally altered minerals. However, when machine learning algorithms are applied to the shortwave infrared (SWIR) bands of an ASTER reflectance image (AST_07XT), misclassification of Mg-OH group minerals is the major source of errors. In this study, an ASTER emissivity image (AST_05) and the AST_07XT SWIR bands are integrated to map minerals in the Duolong area, Tibetan Plateau. The results show that ASTER thermal infrared (TIR) bands can successfully identify Mg-OH group minerals. To improve the performance of classification, a novel voting-based extreme learning machine (V-ELM) algorithm is introduced to map hydrothermally altered minerals. The classification based on the ASTER SWIR-TIR data gets good identification of Mg-OH group minerals, which is better than those acquired using SWIR and TIR data alone. Moreover, these results also show that the AST_05 TIR bands cannot discriminate Al-OH group minerals. Compared with the classification applied to AST_07XT SWIR data, the classification applied to the ASTER SWIR-TIR data can achieve a higher overall accuracy (99.01%). The ASTER data results are spatially consistent with those of the Hyperion data. In accordance with the image processing results, a new deposit associated with felsic intrusions has been validated by field investigations. These schemes are promising for mineral mapping in the Tibetan Plateau.

Selecting optimal bands for sub‐pixel target detection in hyperspectral images based on implanting synthetic targets

Target detection at sub-pixel abundances is, in fact, one of the challenging issues of hyperspectral image processing. Selection of optimal bands to improve sub-pixel target detection (STD) performance is one of the common solutions, applied by many researchers. Nevertheless, the absence of sufficient training data is the main weakness of selecting optimal bands with regard to this approach. The present research introduces a new band selection method for STD in hyperspectral images, based on creating training data, in which the desired target spectrum is implanted randomly in a series of host pixels from the entire hyperspectral image. Afterwards, via running an optimisation algorithm twice, with the aim of minimising the false alarm rate (FAR) in local adaptive coherence estimator target detection algorithm, the number of optimal bands and optimal spectral bands are selected. In this study, the performance of three optimisation methods including the genetic algorithm (GA), Grey Wolf optimisation (GWO), and particle swarm optimisation (PSO) are compared. Experimental results on HyMap and Hyperion datasets show that the proposed method obtains the minimum FAR compared with the rest of the evaluated methods. Also, based on the results obtained, GWO outperforms GA and PSO optimisation methods in the STD domain.

Sensitivity analysis of pansharpening in hyperspectral change detection

Change detection (CD) is one of the most important uses of remote sensing, and it plays a key role in many applications. Satellite hyperspectral imagery has a high spectral resolution but low spatial resolution, which results in images with mixed pixels. To improve spatial resolution in hyperspectral images, multiresolution fusion techniques must be used, one which is called pansharpening (PS). This paper investigates the sensitivity and performance of CD methods by fusing Advanced Land Imager and Hyperion datasets based on a PS algorithm. Three different CD algorithms are used here for that purpose: cross-covariance (CC), cross equalization (CE), and principal component analysis (PCA). In addition, Gram-Schmidt (GS), HySure, and PCA are utilized as the PS methods of choice. The CD results obtained from both the original hyperspectral data and from the spatially fused data are compared to reveal the potential of PS in CD applications. Furthermore, the presented procedure also shows that the HySure method in particular yields good results for the CD.

Identification of hydrothermal alteration minerals associated with geothermal system using ASTER and Hyperion satellite data: a case study from Yankari Park, NE Nigeria

Concealed and fossilized geothermal systems are not characterized by obvious surface manifestations like hotsprings and fumaroles, therefore, could not be easily identifiable using conventional techniques. In this investigation, the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion data-sets were evaluated in discriminating hydrothermal alteration minerals associated with geothermal systems as a proxy in identifying subtle Geothermal systems at Yankari Park, Nigeria. Feature-oriented principal component selection, spectral angle mapper, linear spectral unmixing were applied to ASTER data based on spectral characteristics of hydrothermal alteration key minerals for a systematic selective extraction of the information of interest. Analytical imaging and geophysics-developed processing methods were applied to Hyperion data for mapping iron oxide/hydroxide minerals and clay mineral assemblages in hydrothermal alteration zones. The results indicate that ASTER and Hyperion could be complemented for reconnaissance stage of targeting subtle alteration mineral assemblages associated with geothermal systems.

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector.

VEGA-CONSTELLATION TOOLS TO ANALIZE HYPERSPECTRAL IMAGES

Abstract. Creating high-performance means to manage massive hyperspectral data (HSD) arrays is an actual challenge when it is implemented to deal with disparate information resources. Aiming to solve this problem the present work develops tools to work with HSD in a distributed information infrastructure, i.e. primarily to use those tools in remote access mode. The main feature of presented approach is in the development of remotely accessed services, which allow users both to conduct search and retrieval procedures on HSD sets and to provide target users with tools to analyze and to process HSD in remote mode. These services were implemented within VEGA-Constellation family information systems that were extended by adding tools oriented to support the studies of certain classes of natural objects by exploring their HSD. Particular developed tools provide capabilities to conduct analysis of such objects as vegetation canopies (forest and agriculture), open soils, forest fires, and areas of thermal anomalies. Developed software tools were successfully tested on Hyperion data sets.

VEGA-CONSTELLATION TOOLS TO ANALIZE HYPERSPECTRAL IMAGES

Creating high-performance means to manage massive hyperspectral data (HSD) arrays is an actual challenge when it is implemented to deal with disparate information resources. Aiming to solve this problem the present work develops tools to work with HSD in a distributed information infrastructure, i.e. primarily to use those tools in remote access mode. The main feature of presented approach is in the development of remotely accessed services, which allow users both to conduct search and retrieval procedures on HSD sets and to provide target users with tools to analyze and to process HSD in remote mode. These services were implemented within VEGA-Constellation family information systems that were extended by adding tools oriented to support the studies of certain classes of natural objects by exploring their HSD. Particular developed tools provide capabilities to conduct analysis of such objects as vegetation canopies (forest and agriculture), open soils, forest fires, and areas of thermal anomalies. Developed software tools were successfully tested on Hyperion data sets.

Derivation of the Green Vegetation Fraction of the Whole China from 2000 to 2010 from MODIS Data

Abstract The green vegetation fraction Fg, which represents the horizontal density of live vegetation, is an important parameter for the study of global energy, carbon, hydrological, and biogeochemical cycling. A common method of calculating Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI, , and full vegetation NDVI, . However, many uncertainties exist for the determination of these parameters at large scales. The present study investigates how and determination can impact Fg calculations for all of China, based on different land-cover datasets, hyperspectral data, and soil type classification maps. The results show the following: 1) The regional ChinaCover dataset, with higher accuracy and more detailed classification, is preferable for calculating Fg in China, compared with the most commonly used MOD12Q1 dataset, although it would not lead to too much difference in values. 2) The soil NDVI from Hyperion datasets shows that soils have highly variable NDVI values (0.006–0.2), and 79.36% of the area studied has a much larger NDVI value than the commonly used value of 0.05. Therefore, the dynamic values with different soil types are much better for Fg calculation than the invariant value (0.05), which would yield a significant overestimation of Fg, especially for areas with low vegetation coverage. 3) A high-quality Fg dataset for China from 2000 to 2010 was established with and parameters based on MOD13Q1 250-m NDVI data.

Oil and gas reservoir exploration based on hyperspectral remote sensing and super-low-frequency electromagnetic detection

This paper proposes a method that combined hyperspectral remote sensing with super-low-frequency (SLF) electromagnetic detection to extract oil and gas reservoir characteristics from surface to underground, for the purpose of determining oil and gas exploration target regions. The study area in Xinjiang Karamay oil–gas field, China, was investigated. First, a Hyperion dataset was used to extract altered minerals (montmorillonite, chlorite, and siderite), which were comparatively verified by field survey and spectral measurement. Second, the SLF electromagnetic datasets were then acquired where the altered minerals were distributed. An inverse distance weighting method was utilized to acquire two-dimensional profiles of the electrical feature distribution of different formations on the subsurface. Finally, existing geological data, field work, and the results derived from Hyperion images and SLF electromagnetic datasets were comprehensively analyzed to confirm the oil and gas exploration target region. The results of both hyperspectral remote sensing and SLF electromagnetic detection had a good consistency with the geological materials in this study. This paper demonstrates that the combination of hyperspectral remote sensing and SLF electromagnetic detection is suitable for the early exploration of oil and gas reservoirs, which is characterized by low exploration costs, large exploration areas, and a high working efficiency.

A Comparison Study of ORASIS and VCA for Mineralogical Unmixing of Hyperspectral Data

Hyperspectral datasets are usually composed of different pure materials or endmembers. Endmember extraction is the most challenging stage in the spectral unmixing procedure. In the case of mineral detection studies, due to the absence of pure pixels for some constituent minerals, the common methods based on pure pixel assumption do not yield accurate results. The current study aims to evaluate the efficiency of ORASIS in endmember determination. ORASIS is a collection of stepwise algorithms working together to produce a set of endmembers not necessarily included in the dataset. The ORASIS method was applied to estimate the endmembers of three hyperspectral dataset series: 1) highly mixed synthetic dataset with maximum purity of 0.48 %, 2) AVIRIS dataset of Cuprite, Nevada, 3) Hyperion dataset of the Dost-Bayli area, Ardabil. SAM and SFF techniques were used to identify the unknown endmembers by comparison of them with USGS spectral library. The functionality of the VCA and PPI as pure pixel-based approaches were also investigated along with ORASIS. The number of estimated endmembers of ORASIS that were appropriately matched with reference spectra was more than VCA and PPI so that majority of detected minerals are present in the study areas. So, it can be inferred that ORASIS generally outperforms the VCA and PPI. The Linear Spectral Unmixing algorithm was implemented for demixing of the Cuprite and Dost-Bayli datasets; and abundances maps were generated using mapping methods. The results highly correspond to the geological, geochemical and mineralogical reports of both regions.

Detection of Alteration Minerals Using Hyperion Data Analysis in Lahroud

The study aims to detect alteration indicative minerals on a part of Hyperion scene in the Lahroud region using the image processing methods. However, it is rarely possible to find actually pure pixels in the mineralogical scale inside the study scenes. This implies the necessity of the identification of sub-pixel materials before classification and mineral mapping using the spectral unmixing algorithms. The Linear Mixture Model (LMM) based standardized hyperspectral processing methodology was employed for this purpose. The necessary pre-processing tasks including the atmospheric and topographic corrections and data quality assessment were also utilized to increase the classification accuracy. The mineralogical and alteration map of the study area was then extracted and evaluated quantitatively with respected to the geological setting of the study area. Despite of the presence of complex facies in the region, the possibility of the applied methodology in the alteration mapping by linear unmixing was proved on Hyperion datasets. The low signal to noise ratio of the Hyperion sensor caused some difficulties but, considering the high cost and consumed time of the field sampling and geochemical studies, the applied method is an advantageous tool for primary steps of the exploration.