Simulation of high-resolution mid-infrared (3–5 μm) images using an atmosphere radiative transfer analytic model

Future mid-infrared satellite missions exploring the Earth will feature advanced high spatial resolution and directional imaging instruments. Consistent end-to-end simulation of them is an important task, and is sometimes the only way to adapt and optimize a sensor and its observation conditions, to choose and test algorithms for data processing, to estimate errors and to evaluate the capabilities of the whole sensor system. However, contrary to other wavelength ranges, the mid-infrared is highly dependent on atmospheric scattering and emission. Therefore, simulation of atmospheric radiative transfer for remote sensing images will remain a challenging task, because few studies on this topic include a full treatment of atmospheric effects. With a given resolution and directional capabilities of the instrument, and combining with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel, coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contributions of atmosphere were considered. Based on different atmospheric conditions and geometrical relations between the scene, the Sun and the sensor, simulated TOA radiance images were produced according to simulated workflows, 10-m spatial resolution and a spectral range of 3.5–3.9 μm. Analysis of results indicates that the analytic model and adjacency effect model are more suitable for mid-infrared imaging simulation than other existing models. This paper describes the principle of the two models, the applied methodology, the set-up of the actual image simulations, and then discusses the final results obtained.     

Using low‐spectral‐resolution images to acquire simulated hyperspectral images

We propose a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. Hyperspectral images provide more spectral information than low‐spectral‐resolution images, because of the additional spectral bands used for data acquisition in hyperspectral imaging. Unfortunately, original hyperspectral images are more expensive and more difficult to acquire. However, some research questions require an abundance of spectral information for ground monitoring, which original hyperspectral images can easily provide. Hence, we need to propose a method to acquire simulated hyperspectral images, when original hyperspectral images are especially necessary. Since low‐spectral‐resolution images are readily available and cheaper, we develop a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. With simulated hyperspectral images, we can acquire more ‘hidden’ information from low‐spectral‐resolution images. Our method uses the principles of pixel‐mixing to understand the compositional relationship of spectrum data to an image pixel, and to simulate radiation transmission processes. To this end, we use previously obtained data (i.e. spectrum library) and the sorting data of objects that are derived from a low‐spectral‐resolution image. Using the simulation of radiation transmission processes and these different data, we acquire simulated hyperspectral images. In addition, previous analyses of simulated remotely sensed images do not use quantitative statistical measures, but use qualitative methods, describing simulated images by sight. Here, we quantitatively assess our simulation by comparing the correlation coefficients of simulated images and real images. Finally, we use simulated hyperspectral images, real Hyperion images, and their corresponding ALI images to generate several classification images. The classification results demonstrate that simulated hyperspectral data contain additional information not available in the multispectral data. We find that our method can acquire simulated hyperspectral images quickly.     

Exploring spectral discrimination of grass species in African rangelands

The spectral reflectance from eight species of rangeland grasses in the Masai Mara Nature Reserve, Kenya, was measured using a laboratory-based spectrometer. There were statistically significant differences in the spectral reflectance between species-a result which is encouraging for future work on identifying, classifying, mapping and monitoring rangeland ecosystems from hyperspectral imagery. To date, hyperspectral imagery has been available only through airborne scanners, but the European Space Agency and the United States' National Aeronautics and Space Administration (NASA) both plan satellite missions. The second part of this paper describes the acquisition and analysis of hyperspectral data (CASI) coincident with ground plots. In these plots, the mix of grass species varied from pure (monospecific) patches through to mixes of four to five different species. Evidence is presented indicating that some species may be identified on the image, based on the laboratory-obtained spectra.     

Comparison between in situ and MODIS-derived spectral reflectances of snow and sea ice in the Amundsen Sea,Antarctica

The spectral albedo and directional reflectance of snow and sea ice were measured on sea ice of various types, including nilas, grey ice, pancake ice, multi-year pack ice, and land-fast ice in the Ross, Amundsen and Bellingshausen seas during a summer cruise in February through March 2000. Measurements were made using a spectroradiometer that has 512 channels in the visible and near-infrared (VNIR) region in which 16 of the 36 bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are covered. Directional reflectance is also retrieved from the MODIS radiometrically calibrated data (Level 1B) concurrently acquired from the first National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellite, Terra. The locations of the ground ice stations are identified accurately on the MODIS images, and the spectral albedo and directional reflectance values at the 16 VNIR MODIS bands are extracted for those pixel locations. MODIS-derived reflectance is then corrected for the intervening atmosphere whose parameters are retrieved from the MODIS atmospheric profiles product (MOD07_L2) for the same granule. The corresponding spectral albedo and directional reflectance with the same viewing geometry as MODIS are derived from our ground-based spectroradiometer measurements. Because the footprint of the ground spectroradiometer is much smaller than the pixel sizes of MODIS images, the averaged spectral reflectance and albedo in the vicinity of each ice station are simulated for the corresponding MODIS pixel from the ground spectral measurements by weighting over different surface types (various ice types and open water). An accurate determination of ice concentration is important in deriving ground reflectance of a simulated pixel from in situ measurements. The best agreement between the in situ and MODIS measurements was found when the ground had 10/10 ice concentration (discrepancy range 0.2–11.69%, average 4.8%) or was oneice-type dominant (discrepancy range 0.8–16.9%, average 6.2%). The more homogeneous the ground surface and the less variable the ground topography, the more comparable between the in situ and satellite-derived reflectance is expected.     

Surface Emissivity Image Simulationfor Atmospheric Absorption Bands based on Spectral Mixing

A simulation method based on spectral mixing is proposed for surface emissivity image generation in atmospheric absorption bands,in order to provide surface input data for the corresponding end to end image simulation.First,endmember selection is conducted on data source to acquire image endmember spectra.Then,substitute endmembers are selected from surface measured spectra by spectral matching with image endmembers,and used for abundance inversion.Finally,using emissivity spectra of substitute endmember in the absorption bands and abundance maps,emissivity images are simulated based on linear spectral mixing model.In the simulation experiment,Landsat8 OLI images were used as data source,and JHU and USGS spectral library data were assumed to be ground spectra of the test case.Since actual emissivity images in absorption bands are unavailable,accuracy analysis is conducted by comparing OLI reflectance images with its simulations generated by the proposed method.Total RSMEs of simulated OLI images are 0.045 and 0.049,respectively;which shows the image simulation method is feasible and can produce images with high accuracy.     

Local radiometric correction of digital aerial photographs for multi source forest inventory

Digitized colour infrared images are a potential information source for multi-source forest inventory applications and particularly for estimating the forest characteristics of relatively small areas. The main problem in computer-aided image analysis of aerial photographs is the presence of bidirectional reflectance, which causes the spectral values of the image pixels to depend on their location in the image. Because of this, and the lack of operational methods for radiometric correction, the full potential of aerial photographs has not been utilised. This paper presents a local radiometric correction method that can be used for reducing the effect of bidirectional reflectance. The method employs satellite images that are less affected by the bidirectional reflectance for the local adjustment of the registered pixel values of aerial photographs. Because of the different spatial resolution of the aerial and satellite imagery, the local correction coefficients are computed for units that are larger than a single aerial photo pixel. In this way, the general level of brightness of the correction units can be determined on the basis of the satellite imagery while retaining the finer spatial resolution of the original aerial photo. The main advantage of the suggested method is that it does not require complex mathematical models for simulating the effect of bidirectional reflectance, neither does it require a priori knowledge of the actual forest attributes in the inventory area, but relies only on the image data.     

基于地表参数真实性的GF-1和SPOT-7多光谱影像质量评价

目的 遥感影像中地表信息表达真实程度决定了影像信息提取和定量化应用水平,传统的从像素灰度和视觉特性角度的影像质量评价方法难以评价影像对地表信息表达能力,本文从地表反射率和NDVI（normalized difference vegetation index）两种地表参数真实性角度评价GF-1和SPOT-7多光谱影像质量。方法 提出了一种基于地表参数真实性的多光谱影像质量评价方法,完成GF-1和SPOT-7卫星对实验区同步成像,地面同步测量大气光学特性和典型地物样区光谱,获取同步观测数据并对多光谱影像进行辐射误差处理,计算地物样区在影像上的反射率和NDVI,通过与地面实测光谱数据比较分析了地表参数真实性,评价GF-1和SPOT-7多光谱影像质量。结果 人工靶标中GF-1影像在4个波段反射率误差均在5%内,精度优于SPOT-7;植被地物中SPOT-7影像在蓝绿红波段反射率误差在4%内,近红外波段误差在15%内,NDVI误差在16%内,反射率和NDVI精度均优于GF-1;硬地地物中GF-1影像在4个波段反射率误差在6%内,精度优于SPOT-7;评价结果表明SPOT-7多光谱影像对植被类地物光谱表达真实度更高,GF-1对硬地类地物光谱表达真实度更高。结论 提出的基于地表参数真实性的遥感影像质量评价方法,能够有效地从地物光谱信息表达精度的角度评价影像质量。     

Synthesis of mixed pixel hyperspectral signatures

The general method of analysing mixed pixel spectral response is to decompose the actual spectra into several pure spectral components representing the signatures of the endmembers. This work suggests a reverse engineering of standardizing the mixed pixel spectrum for a certain spatial distribution of endmembers by synthesizing spectral signatures with varying proportions of standard spectral library data and matching them with the experimentally obtained mixed pixel signature. The idea is demonstrated with hyperspectral ultraviolet–visible–near-infrared (UV–vis–NIR) reflectance measurements on laboratory-generated model mixed pixels consisting of different endmember surfaces: concrete, soil, brick and vegetation and hyperspectral signatures derived from Hyperion satellite images consisting of concrete, soil and vegetation in different proportions. The experimental reflectance values were compared with the computationally generated spectral variations assuming linear mixing of pure spectral signatures. Good matching in the nature of spectral variation was obtained in most cases. It is hoped that using the present concept, hyperspectral signatures of mixed pixels can be synthesized from the available spectral libraries and matched with those obtained from satellite images, even with fewer bands. Thus enhancing the computational job in the laboratory can moderate the keen requirement of high accuracy of remote-sensor and band resolution, thereby reducing data volume and transmission bandwidth.     

A simplified method for retrieval of ground level reflectance of targets from airborne video imagery

This paper describes an efficient method for retrieval of ground reflectance characteristics of targets from calibrated multispectral airborne video data for routine operational airborne missions. The method uses a simplified atmospheric scattering model in combination with a dark-object subtraction procedure to estimate the effect of the atmosphere in the path between the target and the sensor, as well as the adjacent environmental effect, on the radiation signal received by an airborne sensor. The simplicity of the atmospheric scattering model is maintained by the assumption that the air density within the targetsensor path in the lower atmosphere is sufficiently uniform for operations of the Charles Sturt University's (CSU) Multispectral Airborne Video System (MAVS). The MAVS acquires imagery in blue, green, red and near-infrared (NIR) narrow spectral bands. The MAVS is radiometrically calibrated and has a consistent radiometric response in-flight. An important feature of the new method is the coupling of the image based brightness data (DN) of a dark-object and the system radiometric calibration coefficients to determine the path reflectance and the environmental reflectance of the target. The sum of the path reflectance and the environment reflectance is known as haze reflectance. The haze reflectance indicates the amount of atmospheric haze in the airborne imagery. The simplified atmospheric model is then employed to determine the actual ground reflectance of the targets using the haze subtracted apparent (total) reflectance of the target at the altitude of the airborne sensor. The apparent reflectance of the target at the sensor altitude is obtained directly from the image based DN data and the system radiometric calibration coefficients. An interesting aspect of this simplified method is that an estimate of the environmental reflectance can be obtained as a by-product of the atmospheric haze calculation using a dark-object subtraction technique. The retrieved ground reflectance characteristics from calibrated MAVS imagery are now being used routinely for remote quantitative monitoring of agricultural and environmental targets.     

Performance characteristics of the indicator classifier on simulated image data

Classification of remotely sensed imagery into groups of pixels having similar spectral reflectance characteristics is conducted classically by comparing the spectral signature of unknown pixels with those of training pixels of known ground cover type. Thus classification methods use only the spectral characteristics of image data without considering the spatial aspects or the relative location of an unknown pixel with respect to pixels from the training data set. An indicator classifier was introduced in 1992 that combines spatial and spectral information in a decision model. In this Letter the performance of this classifier is tested on simulated image data with known mineral targets and varying spatial variability and noise. It is demonstrated that incorporating spatial continuity into the classification process may largely increase the accuracy of the resulting classified images.     

End-to-end simulation model of rover-based hyperspectral remote-sensing systems: application to VNIS

The simulation of remote-sensing hyperspectral images has various applications such as the design of future hyperspectral imaging systems, understanding of the image formation process, development and validation of data processing algorithms, and optimization of the instrument imaging mode. For incomplete understanding of the lunar surface and the wide environmental differences between earth and moon observation, imaging systems for lunar observation cannot be tested in their exact working environments before launch. In these cases, simulation of lunar hyperspectral images can be used as a powerful tool to analyse the imaging process on the moon. The VIS–NIR imaging spectrometer (VNIS) aboard the Chang’e-3 (CE-3) lunar rover is used to perform in situ mineral detection on the lunar surface, but the rover-based VNIS has some additional effects from the rover itself (e.g. the shadow caused by the rover). In this paper, a rover-based radiative transfer model has been developed, and the simulation model is able to generate realistic VNIS-like data in an automatic way under a set of user-driven instrument and illumination parameters. A realistic surface reflectance cube, as the original input for the simulation model, is provided by the interference imaging spectrometer (IIM) data of Chang’e-1 (CE-1). Several hyperspectral simulations in different illumination and observation geometries have been conducted to analyse the effects of shadow, specular irradiance, and diffuse irradiance on the imaging data. For certain illumination geometries, the simulation model can forecast image quality in different observation geometries; the model can also determine the optimal observation azimuth ranges at different solar elevation angles. Moreover, the simulation model can be used to provide test images for the rover effect elimination algorithms. These applications of the model can facilitate understanding by analysing the rover-based hyperspectral remote-sensing process and eventually obtaining high-quality images of the lunar surface.     

A probabilistic SVM approach for hyperspectral image classification using spectral and texture features

New hyperspectral sensors can collect a large number of spectral bands, which provide a capability to distinguish various objects and materials on the earth. However, the accurate classification of these images is still a big challenge. Previous studies demonstrate the effectiveness of combination of spectral data and spatial information for better classification of hyperspectral images. In this article, this approach is followed to propose a novel three-step spectral–spatial method for classification of hyperspectral images. In the first step, Gabor filters are applied for texture feature extraction. In the second step, spectral and texture features are separately classified by a probabilistic Support Vector Machine (SVM) pixel-wise classifier to estimate per-pixel probability. Therefore, two probabilities are obtained for each pixel of the image. In the third step, the total probability is calculated by a linear combination of the previous probabilities on which a control parameter determines the efficacy of each one. As a result, one pixel is assigned to one class which has the highest total probability. This method is performed in multivariate analysis framework (MAF) on which one pixel is represented by a d-dimensional vector, d is the number of spectral or texture features, and in functional data analysis (FDA) on which one pixel is considered as a continuous function. The proposed method is evaluated with different training samples on two hyperspectral data. The combination parameter is experimentally obtained for each hyperspectral data set as well as for each training samples. This parameter adjusts the efficacy of the spectral versus texture information in various areas such as forest, agricultural or urban area to get the best classification accuracy. Experimental results show high performance of the proposed method for hyperspectral image classification. In addition, these results confirm that the proposed method achieves better results in FDA than in MAF. Comparison with some state-of-the-art spectral–spatial classification methods demonstrates that the proposed method can significantly improve classification accuracies.     

Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction

A method for the radiometric correction of wide field-of-view airborne imagery has been developed that accounts for the angular dependence of the path radiance and atmospheric transmittance functions to remove atmospheric and topographic effects. The first part of processing is the parametric geocoding of the scene to obtain a geocoded, orthorectified image and the view geometry (scan and azimuth angles) for each pixel as described in part 1 of this jointly submitted paper. The second part of the processing performs the combined atmospheric/ topographic correction. It uses a database of look-up tables of the atmospheric correction functions (path radiance, atmospheric transmittance, direct and diffuse solar flux) calculated with a radiative transfer code. Additionally, the terrain shape obtained from a digital elevation model is taken into account. The issues of the database size and accuracy requirements are critically discussed. The method supports all common types of imaging airborne optical instruments: panchromatic, multispectral and hyperspectral, including fore/aft tilt sensors covering the wavelength range 0.35-2.55 w m and 8-14 w m. The processor is designed and optimized for imaging spectrometer data. Examples of processing of hyperspectral imagery in flat and rugged terrain are presented. A comparison of ground reflectance measurements with surface reflectance spectra derived from airborne imagery demonstrates that an accuracy of 1-3% reflectance units can be achieved.     

Close-range hyperspectral imaging for geological field studies: workflow and methods

Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional (3D) models, derived from terrestrial laser scanning (lidar), that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.     

Hyperspectral image classification using multiobjective optimization

Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.

     

一种基于物理机理的高光谱遥感图像真彩色校正模型

针对高光谱遥感影像由于各波段光谱范围窄,难以获得符合人们视觉效果的真彩色合成影像问题,提出一种基于物理机理的高光谱遥感图像真彩色校正模型。该模型充分利用高光谱影像在红、绿、蓝反射区的所有谱段信息,通过插补波段并进行波段加权积分重建真彩色合成图像,进而结合实测地物反射率光谱,利用辐射传输模拟的方式,构建具备普适性的真彩色校正模型。利用航空高光谱遥感影像进行色彩校正实验的结果表明,所构建的真彩色校正模型能够很好地应用于高光谱遥感影像真彩色校正。     

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain,China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.     

Aspects of operational atmospheric correction of hyperspectral imagery

The large number of spectral bands of hyperspectral instruments and the time required for the calculation of atmospheric look-up tables and the reflectance image cube pose very challenging requirements on an operational processing facility. This contribution presents some aspects and suggestions to reduce the processing time. Essential components are a precalculated database with a reduced number of spectral bands, an interactive phase to determine the appropriate atmospheric parameters, and a choice between medium and high accuracy levels for the atmospheric correction. The medium accuracy levels work with look-up tables for a reduced number of spectral bands employing interpolation for the channels omitted in the look-up tables. The high accuracy level uses tables for all channels and includes the scan angle dependence of the atmospheric radiance and transmittance functions. These ideas were successfully implemented and tested during several airborne hyperspectral campaigns resulting in an estimated time saving of a factor 3-7. The deviations of field measured reflectance spectra and spectra retrieved from airborne HyMap imagery are in the range of 2-3% or better.     

A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

This paper presents a new approach to the analysis of hyperspectral images, a new class of image data that is mainly used in remote sensing applications. The method is based on the generalization of concepts from mathematical morphology to multi-channel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profile, which is used for multi-scale analysis of hyperspectral data. This approach is particularly well suited for the analysis of image scenes where most of the pixels collected by the sensor are characterized by their mixed nature, i.e. they are formed by a combination of multiple underlying responses produced by spectrally distinct materials. Experimental results demonstrate the applicability of the proposed technique in mixed pixel analysis of simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Digital Airborne (DAIS 7915) and Reflective Optics System Imaging Spectrometers. The proposed method works effectively in the presence of noise and low spatial resolution. A quantitative and comparative performance study with regards to other standard hyperspectral analysis methodologies reveals that the combined utilization of spatial and spectral information in the proposed technique produces classification results which are superior to those found by using the spectral information alone.     

Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique

The concept of imaging spectrometry, or hyperspectral imaging, is becoming increasingly popular in scientific communities in recent years. Hyperspectral imaging data covering the spectral region between 0.4 and 2.5 μm and collected from aircraft and satellite platforms have been used in the study of the earth's atmosphere, land surface, and ocean color properties, as well as on planetary missions. In order to make such quantitative studies, accurate radiometric and spectral calibrations of hyperspectral imaging data are necessary. Calibration coefficients for all detectors in an imaging spectrometer obtained in a laboratory may need to be adjusted when applied to data obtained from an aircraft or a satellite platform. Shifts in channel center wavelengths and changes in spectral resolution may occur when an instrument is airborne or spaceborne due to vibrations, and to changes in instrument temperature and pressure. In this paper, we describe an algorithm for refining spectral calibrations of imaging spectrometer data using observed features in the data itself. The algorithm is based on spectrum-matching of atmospheric water vapor, oxygen, and carbon dioxide bands, and solar Fraunhofer lines. It has been applied to real data sets acquired with airborne and spaceborne imaging spectrometers.