Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery

Abstract

An algorithm is developed for automatic atmospheric correction of satellite imagery of the Earth's surface. The algorithm is based solely on the satellite image being corrected and on climatology of the area. It is applicable to low resolution (1 km field of view) and high resolution (10-80m field of view) imagery of land areas for the solar spectrum. The algorithm requires that some pixels in the image will correspond to dense dark vegetation as the surface cover. Once the presence of such pixels is established, the algorithm automatically chooses these pixels, derives the atmospheric optical thickness (a measure of the amount of haze) and corrects the image. The algorithm is sensitive to the assumed reflectance of the dense dark vegetation. As a result, the accuracy of the corrected surface reflectance (p) is expected to be δp-±0.01. It is not very sensitive to the assumed aerosol characteristics, the accuracy of satellite calibration or the knowledge of the exact fraction of the image covered by the dense dark vegetation. The correction algorithm was applied to clear and hazy Landsat Multispectral Scanner images of the same area in the Washington D.C. and the Chesapeake Bay region. The aerosol optical thickness (t_a) derived from the imagery shows a good agreement with simultaneous sunphotometer measurements from the ground within δT_a=±0.20 in band 1 (0.5.0.6) and δt_a=±0.05 in band 2 (0.6-0.7μm). The images in the hazy and clear days were corrected and compared. The comparison shows, for example, that the vegetation index was corrected from 0-39 in the clear day and 0-21 in the hazy day to 0-57± 0.01 in these two days. The algorithm, in its present form, can be applied to satellite imagery that includes at least two channels in the visible part of the spectrum, preferably blue and red. Application to the Advanced Very High Resolution Radiometer type of sensor (with one broad channel in the visible part of the spectrum) would need some modifications.     

SIERRA: A new approach to atmospheric and topographic corrections for hyperspectral imagery

In mountainous areas, slope and altitude variations modulate the airborne sensed hyperspectral radiance image. A new algorithm, SIERRA, has been developed for atmospheric, relief and BRDF corrections in order to extract the surface reflectance in the form of bi-hemispherical albedo that does not depend on solar incidence and observation angles. The forward modeling efforts focus on the estimation of diffuse irradiance and upwelling diffuse radiance, and on the formulation of BRDF effects. The inversion scheme consists of four steps, that go deeper and deeper into the phenomena's complexity.To validate the model, reflectance images are assessed from radiance images simulated with different radiative transfer codes or forward models: MODTRAN4 in the case of homogeneous and flat ground, AMARTIS and SIERRA forward models for heterogeneous and mountainous cases. The surface reflectance is retrieved with a 5% relative error under standard acquisition conditions.SIERRA is applied to HyMap data acquired over the hilly landscape near Calanas, Spain. The hypercube reflectances are compared with those obtained using ATCOR4 and COCHISE. The benefit of relief correction is clearly demonstrated.     

Correction of atmospheric and topographic effects for high spatial resolution satellite imagery

A method for the combined correction of atmospheric and topographic effects has been developed. It accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a threedimensional atmosphere. A Digital Elevation Model (DEM) is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated. The method is restricted to high spatial resolution satellite sensors like Landsat TM and SPOT HRV, since some simplifying assumptions are being made to reduce the required image processing time. The possibilities and limitations of the method are critically discussed.     

Fields of non-linear regression models for atmospheric correction of satellite ocean-color imagery

Remote sensing of ocean color from space, a problem that consists of retrieving spectral marine reflectance from spectral top-of-atmosphere reflectance, is considered as a collection of similar inverse problems continuously indexed by the angular variables influencing the observation process. A general solution is proposed in the form of a field of non-linear regression models over the set T of permitted values for the angular variables, i.e., as a map from T to some function space. Each value of the field is a regression model that performs a direct mapping from the top-of-atmosphere reflectance to the marine reflectance. Since the spectral components of the field take values in the same variable vector space, the retrievals in individual spectral bands are not independent, i.e., the solution is not just a juxtaposition of independent models for each spectral band. A scheme based on ridge functions is developed to approximate this solution to an arbitrary accuracy, and is applied to the retrieval of marine reflectance in Case 1 waters, for which optical properties are only governed by biogenic content. The statistical models are evaluated on synthetic data as well as actual data originating from the SeaWiFS instrument, taking into account noise in the data. Theoretical performance is good in terms of accuracy, robustness, and generalization capabilities, suggesting that the function field methodology might improve atmospheric correction in the presence of absorbing aerosols and provide more accurate estimates of marine reflectance in productive waters. When applied to SeaWiFS imagery acquired off California, the function field methodology gives generally higher estimates of marine reflectance than the standard SeaDAS algorithm, but the values are more realistic.     

Normalization of satellite imagery

Abstract

Sets of Thematic Mapper (TM) imagery taken over the Washington DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal colour changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band colour imagery can be directly interpreted for quantitative information of the target.     

Synthetic satellite imagery for current and future environmental satellites

During the next decade, data from a new generation of US geostationary and polar orbiting satellites will become available. To prepare for these data, representative imagery of these satellites is desirable. Two independent methods have been developed to create imagery from future satellites before they are placed into orbit. One method uses data from current operational and experimental satellites. Data obtained this way are referred to as simulated imagery. Another method generates satellite imagery by using numerical models. Data obtained by this method are referred to as synthetic imagery. Each method has some weaknesses that can be overcome by using both methods together. Synthetic imagery for two future US sensors is introduced in this paper. Emphasis is placed on a severe thunderstorm event.     

Line-based rational function model for high-resolution satellite imagery

The object-to-image transformation of high-resolution satellite images often involves a rational functional model (RFM). Traditionally, RFM uses point features to obtain the transformation coefficients. Since control lines offer greater flexibility than control points, this study proposes a new RFM approach based on linear features. The proposed methods include direct RFM and bias-compensated RFM using control lines. The former obtains the rational polynomial coefficients (RPCs) directly from control lines, whereas the latter uses sensor-orientated RPCs and control lines to determine compensated coefficients. The line-based RFMs include vector and parametric line representations. The experiments in this study analysed the effects of line number, orientation, and length using simulation and real data. The real data combined three-dimensional building models and high-resolution satellite images, such as IKONOS and QuickBird images. Experimental results show that the proposed algorithms can achieve pixel-level accuracy.     

Model for atmospheric corrections to microwave brightness temperature data

The effects of atmospheric liquid water (cloud) and water vapor on the brightness temperature data acquired by Bhaskara-Satellite Microwave Radiometer at 19.1 and 22.235 GHz near the nadir look angle have been discussed in detail. It has been observed that the net contribution to brightness temperature data due to atmosphere is quite considerable over sea and wet land conditions. Two operational quasistatistical models have been developed for doing atmospheric corrections to brightness temperature data, one applicable under cloud-free conditions and the other under all weather conditions. The physical concepts to arrive at these quasistatistical models have been discussed. The constants of the models are estimated by the method of least squares fit by simulating 200 sets of atmospheric and ground conditions. The root mean square errors in the brightness temperature after applying atmospheric corrections are estimated to be 0.5 and 4 K under cloud-free and all weather conditions respectively. A detailed discussion on the validity and applicability of the models is also presented.     

Estimation of variable atmospheric parameters for the atmospheric correction of satellite images

The information about variable components of the atmosphere (aerosol, water vapour, and ozone) during acquisition is required for the atmospheric correction of spectral images acquired by shortwave sensors of the Earth observing remote-sensing satellites. The procedure to estimate aerosol optical depth and columnar water vapour by the inversion of the atmospheric radiative transfer model 6S using moderate-resolution spectra of incident solar radiation is proposed. Comparison to the results obtained by the Aerosol Robotic Network AERONET at an AERONET site at the distance of 50 km on days when both sensors were in the same air mass shows systematic overestimation both of aerosol optical depth and of columnar water vapour if aerosol optical depth is estimated in the wavelength range of 365–425 nm and columnar water in the range of 895–985 nm using spectra of total irradiance. If more wavelengths and diffuse-to-total spectral irradiance ratio are implemented in the inversion, the bias of estimated water vapour decreases, but aerosol optical depth is underestimated. The estimates at 50 km distance are well correlated. The modelled spectral irradiance using estimated atmospheric parameters matches the measured spectra with high accuracy. In the spectral bands of the Sentinel-2 MultiSpectral Instrument (MSI), the differences do not exceed 2%.     

A hybrid chaotic blowfish encryption for high-resolution satellite imagery

Multimedia Tools and Applications - In this article, we have designed a new information confidentiality mechanism based on the combination of Blowfish encryption algorithm along with Henon and Chen...     

Generalized Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for daytime imagery

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.     

Searching satellite imagery with integrated measures

Due to the advances in imaging and storage technologies, the number and size of images continue to grow at a rapid pace. This problem is particularly acute in the case of remotely sensed imagery. The continuous stream of sensory data from satellites poses major challenges in storage and retrieval of the satellite imagery. In the mean time, the ubiquity of Internet has resulted into an ever-growing population of users searching for various forms of information. In this paper, we describe the search engine SIMR—Satellite Image Matching and Retrieval system. SIMR provides an efficient means to match remotely sensed imagery. It computes spectral and spatial attributes of the images using a hierarchical representation. A unique aspect of our approach is the coupling of second-level spatial autocorrelation with quad tree structure. The efficiency of the web-based SIMR has been evaluated using a database of images with known characteristics: cities, towns, airports, lakes, and mountains. Results show that the integrated signature can be an effective basis for accurately searching databases of satellite based imagery.     

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.     

The earth location of geostationary satellite imagery

Spin scan cloud pictures from NASA's Applications Technology Satellites are being computer processed as an operational experiment. Details of this near-real-time image manipulating activity are described. Processing involves the treatment of horizon and landmark features in support of the photogrammetric aspects and the subsequent transformation of images onto standard map projections.     

Disturbance rejection for an aircraft flying in atmospheric disturbances

For an aircraft flying in atmospheric disturbances a static state feedback controller is designed. The design goal is that of rejecting the influence of a rotary gust disturbance to the vertical and forward velocity of the aircraft. The necessary and sufficient condition for this problem to have a solution is derived in terms of simple forms involving only the flight stability derivatives of the aircraft. The general analytical expression of the disturbance rejection controller matrix and the resulting closed, loop system, are derived in forms involving the aircraft parameters as well as arbitrary design parameters. The necessary and sufficient conditions for disturbance rejection with simultaneous stabilizability of the closed loop flight system are also established.     

ALADIN and WRF forecasting evaluation using satellite imagery

Simulated satellite images are a good indicator of the state of the atmosphere described by the fields predicted by numerical weather prediction (NWP) models. Therefore, in order to control NWP operational models used by the Algerian meteorological service, especially over the desert region, Meteosat Second Generation (MSG) simulated images of brightness temperature (BT) were generated from ALADIN (Aire Limitée Adaptation Dynamique Development International) and WRF (Weather and Research Forecasting) outputs using the Radiative Transfer for TIROS Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV9) model. As reference data, MSG images were used to compute certain deterministic and probabilistic statistical parameters. This version of the RTTOV model assimilates cumuliform clouds, stratiform clouds and those of upper levels, such as cirrus. This comparative study shows that WRF reproduces BTs well where they exist, but raises too many false alarms for very cold BTs with values of bias around 1. The number of false alarms greatly affects the quality of Heidke skill scores (HSS), unlike the ALADIN model, which reveals fewer false alarms but detects events less well. With low values of bias for the lowest temperatures, ALADIN HSS scores are better than those of WRF for the lowest BTs. The double-penalty impact is slightly lessened for local and convective cloud by about 10% with WRF compared with ALADIN.     

An application of satellite imagery for highway maintenance and rehabilitation in Niger

Recent studies in the Republic of Niger have demonstrated that information acquired from the interpretation of satellite imagery can play a significant role in the planning, management and implementation of highway maintenance or rehabilitation in developing countries. In addition to assisting overall alignment and design review, LANDSAT MSS data proved invaluable with respect to understanding physical causes of pavement failure and the engineering options required to overcome repetition of similar problems in the future.     

Fourier Series for analysis of temporal sequences of satellite sensor imagery

Fourier Series and the derivative were used in this study for analysing time series of remotely-sensed data. The technique allows fundamental characteristics of time series data to be quantified. In Fourier analysis a function in space or time is broken down into sinusoidal components, or harmonics. The first and second harmonics are a function of the mono or bi-modality of the curve, demonstrated in the study on Global Vegetation Index data classified into typical mono and bi-modal vegetation index zones. The last harmonic explains close to 100 per cent of the variance in the curve. Other important parameters of the time series, such as extreme points and rate of change, can be extracted from the derivative of the Fourier Series. Fourier Series may form a basis for a quantitative approach to the problem of handling temporal sequences of remotely-sensed data.     

Automatic system for operational traffic monitoring using very-high-resolution satellite imagery

Vehicle detection from very-high-resolution satellite imagery has received increasing interest during the last few years. In this article, we propose an automatic system for operational traffic monitoring using very-high-resolution optical satellite imagery (0.5–0.6 m resolution) of small highways with low traffic density and a range of different illumination conditions, including cloud-shadowed, hazy, and partially cloudy conditions. The proposed system includes cloud and cloud shadow detection, road detection, and vehicle detection, classification, and counting. The main part of the system is vehicle detection, which is constructed using an elliptical blob detection strategy followed by region growing and feature extraction steps. Vehicular objects are separated from non-vehicular objects using a K-nearest-neighbour classifier, with various classical features used for pattern recognition, as well as some proposed application-specific features, and are also classified according to vehicle size. The fully automatic processing chain has been validated on a selection of satellite scenes from different parts of Norway, including imagery with large amounts of cloud, fog, cloud shadows, and similar conditions that complicate image interpretation. The overall vehicle detection rate was 85.4% and the false detection rate was 9.2%. Overall, this demonstrates the potential of operational traffic monitoring using very-high-resolution satellites.