Hyperion Sensor Research Articles

Hyperspectral Image Sharpening Using Fusion Techniques -A Case Study at Salah Al-Din Province/Iraq-

The data fusion process includes merging two or more pieces of information obtained from different sensors. Satellite image fusion research aims to create a new image by combining two images captured by different sensors using various methodologies. In this research, image sharpening tools were used to combine a hyperspectral image with a low spatial resolution captured by a Hyperion sensor mounted on the Earth Observation 1 (EO-1) satellite with a grayscale high spatial resolution image captured by Enhanced Thematic Mapper Plus (ETM +) sensor mounted on Landsat-8 (resampling first one to ensure equal spatial resolution of both images). In addition, three techniques were adopted for implementing the Fusion mechanism: the Principal Component Analysis PCA, the Nearest Neighbor Diffusion NNDifuse, and the Gram-Schmidt method; these were used to sharpen hyperspectral data using high spatial resolution. The result showed that the Gram-Schmidt method could give Hyperspectral images with higher spectral and spatial resolution in panchromatic image data more accurately than the other methods.

MV-CDN: Multi-Visual Collaborative Deep Network for Change Detection of Double-Temporal Hyperspectral Images

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal.

A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery

In this study, an automatic Change Detection (CD) framework based on a multi-dimensional deep Siamese network was proposed for CD in bi-temporal hyperspectral imagery. The proposed method has two main steps: (1) automatic generation of training samples using the Otsu algorithm and the Dynamic Time Wrapping (DTW) predictor, and (2) binary CD using a multidimensional multi-dimensional Convolution Neural Network (CNN). Two bi-temporal hyperspectral datasets of the Hyperion sensor with a variety of land cover classes were used to evaluate the performance of the proposed method. The results were also compared to reference data and two state-of-the-art hyperspectral change detection (HCD) algorithms. It was observed that the proposed method relatively had higher accuracy and lower False Alarm (FA) rate, where the average Overall Accuracy (OA) and Kappa Coefficient (KC) were more than 96% and 0.90, respectively, and the average FA rate was lower than 5%.

CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection

Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity disparity to the pixels of two states, in this paper, we propose a novel change detection scheme served by sensitivity disparity networks (CD-SDN). The CD-SDN is proposed for detecting changes in bi-temporal hyper-spectral images captured by the AVIRIS sensor and HYPERION sensor over time. In the CD-SDN, two deep learning frameworks, unchanged sensitivity network (USNet) and changed sensitivity network (CSNet), are utilized as the dominant part for the generation of binary argument map (BAM) and high assurance map (HAM). Then two approaches, arithmetic mean and argument learning, are employed to re-estimate the changes of BAM. Finally, the detected results are merged with HAM and obtain the final detected binary change maps (BCMs). Experiments are performed on three real-world hyperspectral image datasets, and the results indicate the good universality and adaptability of the proposed scheme, as well as its superiority over other existing state-of-the-art algorithms.

An external calibrator system for the hyperion sensors

The NCPA has developed an external calibrator system which is nearing maturity. This calibrator is attached as a replacement sensor lid for the Hyperion sensor, and injects the signal into the back volume of the Hyperion sensor. When the external calibrator is mated to the sensor, the integrated sensor-external calibrator package behaves as a self-calibrating sensor. The external calibrator allows the Hyperion to operate nominally and without significant change in response with the external calibrator installed. The calibrator is driven by an external signal generated by the digitizer (e.g., the CAL signal on a GEOTECH). This system is capable of producing signals with amplitudes greater than 20-Pa in the operational environment between 0.01 and 10 Hz. We report here on the performance metrics (frequency flatness, level linearity, etc.) as well as summarize the theory of operation of the device.

VALIDATION OF SOIL USES AROUND RESERVOIRS IN THE SEMI-ARID THROUGH IMAGE CLASSIFICATION

ABSTRACT The work evaluated the potential for discrimination of land use and occupation around reservoirs, using spectral information obtained by multispectral, hyperspectral satellites and images obtained with an ARP (remotely piloted aircraft). The research analyzed the performance of different images classification techniques applied to multispectral and hyperspectral sensors for the detection and differentiation of soil classes around the Paus Brancos and Marengo reservoirs, located in Settlement 25 of Maio. The classes identified based on surveys in campaigns carried out in 2014 and 2015 around the reservoirs were: water, macrophytes, exposed soil, native vegetation, agriculture, thin and ebbing vegetation, in addition to the cloud and cloud shadow targets. The performance of the classifiers applied to the image of the Hyperion sensor was, in general, superior to those obtained in Landsat 8 image, which can be explained by the high spectral resolution of the first, which facilitates the differentiation of targets with similar spectral response. For validation of the supervised classification method of Maximum Likelihood, Landsat 8 (08/24/2015) and Hyperion (08/28/2015) images were used. The results of the application indicated a good performance of the classifier associated with the RGB composition of the chosen Hyperion image (bands R - 51, G - 161, B - 19) in the detection of the classes around this reservoir, producing a Kappa coefficient of 0.83. The availability of data from the Hyperion sensor is very restricted, which hinders the development of continued research, thus the use of images surpassed by RPA is extremely viable.

Spatial analysis of Hyperion hyperspectral indices to map the vegetation state in the coastal oases of Tunisia

An elevated human presence due to the involvement of the coastal oases of Tunisia in the global petrochemical industry and population pressure in the 1970s has resulted in major changes in the oases’ agro–ecosystem environment. The consequences of this have been urbanisation and rural exodus, priority to the industrial sectors and services at the expense of agriculture, high mobility and rise of trade. The coastal oases of Gabes located in the South-East of Tunisia are considered in this study. This has been affected by sharp degradation, mainly of anthropogenic origins such as demographic growth, extension of the urban areas and creation of a highly contaminating chemical zone amplifying their environmental vulnerability. Satellite data is an essential tool in the study and mapping of these types of environment and for that, we started with the mapping of the vegetative land use using the vegetation indices derived from the hyperspectral scene of the Hyperion sensor (25 April 2010) and field data. This has allowed us to better characterise the most vulnerable areas and to identify the socio–environmental risks. The analysis of the radiometric indices leads to the definition of the spatial extension of vegetation cover in the oases. This study has permitted us to outline the oases’ typologies in Gabes and to discuss their dynamics in the short term.

STUDYING EVOLUTION OF HYDROTHERMAL ALTERATION MATERIALS IN THE TURRIALBA VOLCANO TROUGH MULTISPECTRAL AND HYPERSPECTRAL IMAGES

Abstract. The aim of this work is to develop a geospatial methodology for the analysis of the time evolution of The Turrialba volcano using different automatic imaging techniques compared to expert-based remote sensing techniques. Change detection of hydrothermal alteration materials in relation with time series from multisensor data acquired in spectral ranges of the visible (VIS) and short wave infrared (SWIR) have been calculated. We used for this purpose multispectral and hyperspectral scenes of the Sentinel 2, ALI and Hyperion sensors, respectively, on four dates from 2013 and 2018. This work adopts a multi-source approach, applied to the analysis of the correlations between hydrothermal materials and spectral anomalies in The Turrialba volcano complex, located in The Central Volcanic Range (Costa Rica).An expert-based technique called Crosta’s technique for detecting hydrothermal materials have been applied. We have chosen four variables for generating a different Principal Component Analysis (PCA) for groups of channels, two highly reflective and two highly absorptive for each mineral. We have tested another technique to detect hydrothermal materials based on a discrete spectral profile analysis and an unsupervised data mining approach. In other sense, we have applied an automatic technique called anomaly detection to compare with the hydrothermal materials results. Results are presented as an approach based on a comparison of two different strategies whose main future interest lies in the automated identification of patterns of hydrothermally altered materials without prior knowledge or poor information about the area, which has relevant implications in image-based prospecting.

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.

Target spectrum based feature selection (TSFS): A new method based on chain coding for target detection problems

One of the controversial issues in hyperspectral remote sensing methods for target detection is whether the feature selection will be useful. Generally, feature selection methods are divided into variance based and wavelength based methods. Variance based feature selection methods like information-theory-based methods may eliminate the distinctive features because the distinctive features probably are not statistical principal components. Distinctive features are crucial to distinguish target from background and are maintained in wavelength based methods which concentrate on wavelength information. However, beside to wavelength-based information, target spectrum fluctuations are also critical for target detection. In addition, the wavelength based methods are often time consuming iterative methods with high computational cost. This paper introduces a new feature selection method considering hyperspectral target spectrum. The proposed algorithm has been developed based on Chain coding idea. We proposed Chain Filtering, Chain Encoding, and Chain Statistics as filter, embedded, and wrapper feature selection methods. In this paper, Chain filtering, Chain statistics, and Chain encoding approaches have been compared with different types of feature selection methods such as Principle Component Analysis (PCA) and Minimum Noise Fraction (MNF). Numerical tests have been executed using 4 datasets including Cuprite Nevada and Jasper Ridge datasets from AVIRIS, Botswana, and local datasets of Isfahan province from Hyperion sensor and using Constrained Energy Minimization (CEM) target detection. The results show the accuracy of target detection applying the proposed feature selection method increases from 85% to about 92% for Kaolinite and from 77% to 96% for Buddingtonite in comparison with PCA for Cuprite dataset. Furthermore, the increments more than 5% and 17.5% will be achieved in comparison with MNF, respectively. The results have shown that not only the proposed method overcomes the accuracy decrement issue of feature selection in target detection, but also it improves target detection accuracy by eliminating non-informative features for target detection applications. So feature selection will be an efficient tool for target detection if the applied feature selection method picks out the distinctive features well.

Hyperspectral Sharpening Approaches Using Satellite Multiplatform Data

The use of hyperspectral (HS) data is growing over the years, thanks to the very high spectral resolution. However, HS data are still characterized by a spatial resolution that is too low for several applications, thus motivating the design of fusion techniques aimed to sharpen HS images with high spatial resolution data. To reach a significant resolution enhancement, high-resolution images should be acquired by different satellite platforms. In this article, we highlight the pros and cons of employing real multiplatform data, using the EO-1 satellite as an exemplary case. The spatial resolution of the HS data collected by the Hyperion sensor is improved by exploiting both the ALI panchromatic image collected from the same platform and acquisitions from the WorldView-3 and the QuickBird satellites. Furthermore, we tackle the problem of assessing the final quality of the fused product at the nominal resolution, which presents further difficulties in this general environment. Useful indications for the design of an effective sharpening method in this case are finally outlined.

Hyperspectral multiple-change detection framework based on sparse representation and support vector data description algorithms

Change detection of hyperspectral (HS) images can be a motivating research area because of the dense spectral sampling of the HS dataset. The support vector domain description (SVDD) classifier has been widely used for change detection of multispectral images. Most of the research has focused only on the binary change detection problem. We took advantage of the high spectral resolution of HS images to extract different kinds of changes present in the image. As a result, we proposed a multiple-change detection algorithm for the HS dataset based on the sparse representation binary change mask and SVDD classifiers. Also, a geometric index, D2R2, was presented to integrate the binary SVDD classifiers. The experimental results showed that our proposed algorithm has comparable performance to some deep neural networks, including one-dimensional convolutional neural network (CNN), two-dimensional CNN, and Deep Pattern Net, with overall accuracies of 88.51% and 70.11% on the two Hyperion sensor datasets.

Agricultural Stress Monitoring using Remote Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics

Remote sensing is widely applied in examining the parameters of the state and quality of water. Spectral characteristics of water are strictly connected with the dispersion of electromagnetic radiation by suspended matter and the absorption of radiation by water and chlorophyll a and b.Multispectral sensor ALI has bands within the ranges of electromagnetic radiation: blue and infrared, absent in sensors such as Landsat, SPOT, or Aster. The main goal of the article was to examine the influence of the presence of these bands on water classification accuracy carried out for simulated images ALI, Landsat, Spot, and Aster. The simulation of images was based on the hyperspectral image from a Hyperion sensor. Due to the spectral properties of water, all the operations on the images were carried out for the set of bands in visible and near-infrared (VNIR) spectral range. In the framework of these studies, the impact of removing individual bands or sets of bands on the classification results was tested. Tests were carried out for the area of the water body of the Dobczyce Reservoir. It was observed that the lack of a spectral response in the infrared range of ALI image can reduce the accuracy of a classification by as much as 60%. On the other hand, the lack of blue and red bands in the dataset for the classification decreased the accuracy of water classification by 15% and 10%, respectively.

Lifetime Absolute Calibration of the EO-1 Hyperion Sensor and its Validation

The Earth-Observing One Hyperion sensor was decommissioned on March 20, 2017. Analysis of Libya 4 Pseudo-Invariant Calibration Sites (PICSs) image data acquired from 2004 to final decommissioning indicated statistically significant drifts in sensor response in the bands 8 to 16 (426.82, 436.99, 447.17, 457.34, 467.52, 477.69, 487.87, 498.04, and 508.22 nm) and bands 206 (2213.93 nm), 209 (2244.22 nm), and 210 (2254.22 nm). The estimated yearly drift in these bands ranges between −0.136% and −0.049%. After correction accounting for the estimated drift, the absolute radiometric calibration of the sensor was evaluated through vicarious reflectance-based calibrations performed at the South Dakota State University (SDSU) test site and the Radiometric Calibration Network (RadCalNet) Railroad Valley site using data from 2002 to 2015. Calibration correction coefficients including gain as much as 1.40 and bias as large as 0.132 were found except absorption bands (890–980, 1090–1180, 1305–1520, and 1750–2050 nm). Finally, the yearly drift and calibration correction coefficients were validated by comparison of the banded multispectral Hyperion data after any significant drift and calibration coefficient correction with Landsat 7 data. The validation showed that after drift and calibration coefficient correction, there is no significant gain and bias using different test sites at different signal levels. The drift and calibration correction coefficients of each band are provided in this paper.

Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method

Mineral detection using remote sensing techniques is important since it saves the time and effort of carrying out manual land surveys. In this paper a novel algorithm, which can be used to detect ilmenite using hyperspectral image analysis is discussed. To investigate this task, a hyperspectral image obtained from the Earth Observing-1 (EO-1) satellite’s Hyperion sensor was used. In the proposed algorithm, first, principal component analysis (PCA) was used for dimensionality reduction and an Euclidean distance-based method was used to extract the pixels containing soil. Thereafter, lab spectral data of typical ilmenite deposits were considered as the reference and a correlation factor analysis was carried out to determine the soil pixels, which are most likely to contain ilmenite and those most unlikely to contain ilmenite. Using these two sets of pixels, a training set was constructed to apply Fisher’s discriminant analysis (FDA) in order to separate the dataset into two distinct classes – ilmenite and non-ilmenite. Based on the spectral similarity, each pixel of the image was classified under one of these classes. This paper also introduces a probability-based approach to obtain results that are more accurate. A probability density function was designed considering the spatial distribution of the mineral. Thereafter, classification was done considering the probability measure as well. Lab tests performed on the soil samples collected from the locations, which were detected by the algorithm validate that the algorithm is accurate.

Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images

Deep learning convolutional neural network (CNN) is popular as being widely used for classification of unstructured data. Land use land cover (LULC) classification using remote sensing data can be used for crop identification also. Present study aims to examine the use of deep learning CNN for LULC classification on Indian Pines dataset and for crop identification on our study area dataset. In the present work, AVIRIS sensor’s Indian Pines standard dataset has been used for LULC classification. Study area from Phulambri, Aurangabad, MH, India, has been used for crop classification. Data have been gathered from EO-1 Hyperion sensor. The accuracy of CNN model depends on optimizer, activation function, filter size, learning rate and batch size. Deep learning CNN is evaluated by changing these parameters. It has been observed that deep learning CNN using optimized combination of parameters has provided 97.58% accuracy for the Indian Pines dataset, while 79.43% accuracy for our study area dataset. The empirical results demonstrate that CNN works well in practice for unstructured data as well as for small size dataset.

An effective approach for improving the accuracy of a random forest classifier in the classification of Hyperion data

Random forest (RF) is one of the most powerful ensemble classifiers often used in machine learning applications. It has been found successful on many benchmarked data. However, the performance of an RF model is highly affected by the calibration of the model parameters. It requires optimization of two parameters—(i) size of RF and (ii) number of features. RF is based on the principle of bagging and random selection of relevant features. This paper conveys an effective method in improving classification accuracy of RF. The principal component analysis (PCA) technique was used for dimension reduction of spectral bands whereas correlation-based feature selection (CFS) was used to identify the optimal set of features. RF was initialized by 10 random trees with an increment of 10, with a variable number of features till the model achieved its highest accuracy. The model was tested with variable sample sizes in order to observe the effectiveness. An investigation was carried out on Hyperion sensor data of the Earth Observing-1 (EO-1) satellite. The performance of RF was observed to be significantly enhanced in terms of predictive ability and computational expenses with the optimized set of features and number of random trees as base classifiers. While comparing with the other advanced classifiers like a support vector machine (SVM), multilayer perceptron (MLP) and maximum likelihood classifier (MLC), the optimized RF outperformed all the other classifiers.

A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors

Hyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus, they can be used in a multitemporal framework to detect and discriminate between different kinds of fine spectral change effectively. However, due to the complexity of the problem and the limited amount of multitemporal images and reference data, only a few works in the literature addressed change detection (CD) in HS images. In this paper, we present a novel method for unsupervised multiple CD in multitemporal HS images based on a discrete representation of the change information. Differently from the state-of-the-art methods, which address the high dimensionality of the data using band reduction or selection techniques, in this paper, we focus our attention on the representation and exploitation of the change information present in each band. After a band-by-band pixel-based subtraction of the multitemporal images, we define the hyperspectral change vectors (HCVs). The change information in the HCVs is then simplified. To this end, the radiometric information of each band is separately analyzed to generate a quantized discrete representation of the HCVs. This discrete representation is explored by considering the hierarchical nature of the changes in HS images. A tree representation is defined and used to discriminate between different kinds of change. The proposed method has been tested on a simulated data set and two real multitemporal data sets acquired by the Hyperion sensor over agricultural areas. Experimental results confirm that the discrete representation of the change information is effective when used for unsupervised CD in multitemporal HS data.

THE SUPERSPECTRAL/HYPERSPATIAL WORLDVIEW-3 AS THE LINK BETWEEN SPACEBORNE HYPERSPECTRAL AND AIRBORNE HYPERSPATIAL SENSORS: THE CASE STUDY OF THE COMPLEX TROPICAL COAST

Abstract. Earth observation of complex scenes, such as coastal fringes, is based on a plethora of optical sensors constrained by trade-offs between spatial, spectral, temporal and radiometric resolution. The spaceborne hyperspectral EO-1 Hyperion sensor (decommissioned in 2017) was able to acquire imagery with 10 nm spectral (220 bands) at 30 m spatial resolutions over 1424.5 km2 scenes. Conversely, the widespread unmanned airborne vehicle (UAV) hyperspatial DJI Mavic Pro camera can collect only natural-coloured imagery of 100 nm spectral (3 bands) but at 0.1 m spatial resolution over ∼10 km2 scenes (with a single battery and calm meteo-marine conditions). The spaceborne WorldView-3 (WV3), featured by 60 nm spectral (16 bands) at 0.3 m spatial resolution (when pansharpened) over 1489.6 km2 scenes, has the capacity to bridge both sensors. This study aims at testing the spectral and spatial performances of the WV3 to discriminate 10 complex coastal classes, ranging from ocean, reefs and terrestrial vegetation in Moorea Island (French Polynesia). Our findings show that geometrically- and radiometrically-corrected 0.3-m 16-band WV3 bands competed with (30-m) 167-band Hyperion performance for classifying 10 coastal classes with 2-neuron artificial neural network modelling, while being able to segment objects seized by 0.1-m (3-band) UAV. Unifying superspectral and hyperspatial specificities, the WV3 also leverages hypertemporal resolution, that is to say 1-day temporal resolution, rivalling UAV’s one.