Radiometric Distortions Research Articles

Estimation of Basal Area in West Oak Forests of Iran Using Remote Sensing Imagery

The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A number of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle component analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands. All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the study area without consideration of aspects and with consideration of major geographic aspects by multiple regression analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in correlation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation coefficient(r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with different correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher correlation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear regression improved the results. The assessment of model validity indicated that the proposed models are properly valid.

Spectral Distortion in Lossy Compression of Hyperspectral Data

Distortion allocation varying with wavelength in lossy compression of hyperspectral imagery is investigated, with the aim of minimizing the spectral distortion between original and decompressed data. The absolute angular error, or spectral angle mapper (SAM), is used to quantify spectral distortion, while radiometric distortions are measured by maximum absolute deviation (MAD) for near‐lossless methods, for example, differential pulse code modulation (DPCM), or mean‐squared error (MSE) for lossy methods, for example, spectral decorrelation followed by JPEG 2000. Two strategies of interband distortion allocation are compared: given a target average bit rate, distortion may be set to be constant with wavelength. Otherwise, it may be allocated proportionally to the noise level of each band, according to the virtually lossless protocol. Comparisons with the uncompressed originals show that the average SAM of radiance spectra is minimized by constant distortion allocation to radiance data. However, variable distortion allocation according to the virtually lossless protocol yields significantly lower SAM in case of reflectance spectra obtained from compressed radiance data, if compared with the constant distortion allocation at the same compression ratio.

A hierarchical approach to Compact Airborne Spectrographic Imager (CASI) high-resolution image classification of Little Miami River Watershed for environmental modelling

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed in Ohio, USA, which was one of the largest hyperspectral image acquisitions. A hierarchical approach was employed using two different classification algorithms: ‘image object segmentation’ for level 1 and ‘spectral angle mapper’ (SAM) for level 2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land‐use/land‐cover (LULC) classes were urban/built, corn, soya bean, wheat, forest, dry herbaceous, grass, lentic, lotic, urban barren, rural barren and unclassified. The final phase of processing was completed after an extensive quality assurance and quality control (QA/QC) phase with 902 points. The overall accuracy was 83.9%. The data set was made available for public research and application; certainly, this product represents an improvement over more commonly utilized, coarser spatial resolution data sets such as National Land Cover Data (NLCD).

Change detection without difference image computation based on multiobjective cost function optimization

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing ``changed'' and ``unchanged'' regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of ``changed'' regions and similarity of pixels of ``unchanged'' regions between input multi-temporal images are measured using image quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel in intermediate change detection mask is considered by employing binary information around its 3 \times 3 neighborhood. A multiobjective cost function for the intermediate change detection mask is obtained by considering contextual information, similarity and dissimilarity measures. The final change detection mask is achieved through minimization of the multiobjective cost function via different realizations of intermediate change detection masks produced by the GA. The proposed method does not need to compute difference image from multi-temporal satellite images, thus can be used as a general purpose change detection method for both synthetic aperture radar (SAR) and optical images. Change detection results and comparisons with the state-of-the-art techniques are shown on multi-temporal georeferenced SAR images acquired by ESA ERS-2 on the city of San Francisco, California, and optical images acquired by Landsat 5 TM on part of Alaska.

Ortho-Rectification and Slope Correction of SAR Data Using DEM and Its Accuracy Evaluation

This paper proposes an accurate ortho-rectification and slope correction method for Synthetic Aperture Radar (SAR) images using a digital elevation model (DEM). Since SAR observation is performed in the squint condition, the image is distorted both geometrically and radiometrically (e.g., through foreshortening, range and azimuth shift, layover, radiometric modulation associated with slope, and shadowing). Furthermore, the pixel height cannot be retrieved directly even when orbital data are accurate. The proposed method calculates the geometric and radiometric distortion components from a comparative process between the DEM-based Simulated SAR Image (DSSI) and the SAR slant range image. When applied to Advanced Land Observing Satellite (ALOS) Phased Array Type L-band SAR (PALSAR) data, the geometric accuracy of the ortho-rectified SAR image at the off-nadir angle of 34.3° was high, with a Root Mean Square Error (RMSE) of 11.9 m when evaluated against Ground Control Points (GCPs) deployed globally. The slope correction effectively reduced the radiometric variation caused by the terrain height variation. The proposed method can be applied to a range of SAR data to support a diversity of applications.

The Optimal Choice of Edge-Radiation-Distortion Correction Methods for OMIS-II Hyperspectral Images

Owing to the effects of the bigger Field-of-View(FOV),atmospheric attenuation,non-Lambertian reflectance of ground objects and the sun-object-sensor geometry,the radiance of airborne hyperspectral imaging spectrometer image tends to be distorted along the scanning lines,and hence it is difficult to use these data directly for quantitative or semi-quantitative applications.This study tried to correct the radiometric distortion of hyperspectral images of OMIS-II(Operative Modular Imaging Spectrometer) by using histogram matching and moment matching algorithms.The results indicate that,for space dimension,the two methods are all capable of retrieving the information of the blurred image edge,with no difference between them,whereas for spectrum dimension,moment matching is more fit for edge radiation distortion correction of OMIS-II.

Humidity and air temperature measurement by quantitative infrared thermography

Quantitative infrared thermography is used to measure the humid air conditions in equilibrium with a building surface. The method is based on accurate temperature measuring as the driving parameter of the physical process. At the same time, the thermographic system produces images of the wall surface behind the device and so can measures the wall's temperature, as well. This is a part of a project dealing with the full monitoring of the indoor environment on thermodynamic basis. The use of a special ancillary device called IRpsicro and a dedicated procedure allows the measurement of the air temperature, relative humidity and speed using only IR thermography. This knowledge is fundamental to know and quantify the moisture exchange process between the porous material and the boundary. As a consequence thereof the evaporative process can be studied on quantitative basis. Essentially, IRpsicro enable the wet and dry bulb temperature measurements needed by the psycrometric method. Equations to achieve indirectly the hygrometric quantities of air are presented. Results obtained in laboratory-controlled conditions are compared with standard measurements of temperature and relative humidity. The paper illustrates a current application of the system in order to evaluate the decay of a heritage building covered by frescoes. An accurate global view of the building envelope is produced with images corrected for the geometric and radiometric distortions.

Correction of reflectance anisotropy: a multi-sensor approach

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks.

Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test

The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.

Simultaneous Geometric and Radiometric Adaptation to Dynamic Surfaces With a Mobile Projector-Camera System

Existing geometric and radiometric compensation methods for direct-projected augmented reality focus on static projection surfaces rather than dynamic surfaces (with varying geometry in time). We aim at providing an effective framework for projecting a sequence of augmented reality images onto dynamic surfaces without geometric and radiometric distortion. We present our design of a special pattern image for simultaneous geometric and radiometric compensation and evaluate two different techniques for embedding the pattern image into augmented reality images. The validity of the proposed method is examined through a variety of experiments with a mobile projector-camera system.

Radiometric normalization of sensor scan angle effects in optical remote sensing imagery

Radiometric distortions along the scan line, caused mainly by the atmosphere, reflection properties of the imaged surface objects, and topography are observed frequently in images acquired by optical remote‐sensing sensors, especially airborne sensors with a wide field of view. In practical applications, such as classification or mosaicking of image data, a relative correction or normalization of these radiometric distortions is needed. Therefore, the goal of this paper is to derive a relative correction algorithm for multispectral line scanner images, which allows the spectral radiance values for different scan angles to be converted to radiance values of a defined reference scan angle. Under the simplifying assumption of complete diffuse reflectors, these radiometric distortions are modelled using a linear equation between the radiances of a pair of scan angles for each class of surface objects separately. A linear regression, applied to all these equations, results in a linear model for the radiance correction, a new empirical radiometric correction method, which is a generalization of earlier proposed strictly additive or multiplicative models. The proposed method is based solely on the digital image data. So, no additional information or user interaction is required, and the method is fully automatic. Applications for data acquired with the airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness and potential of the proposed method especially for mosaicking and classification purposes.

A Wavelet-Based Multiresolution Approach to Solve the Stereo Correspondence Problem Using Mutual Information

In this correspondence, we propose a wavelet-based hierarchical approach using mutual information (MI) to solve the correspondence problem in stereo vision. The correspondence problem involves identifying corresponding pixels between images of a given stereo pair. This results in a disparity map, which is required to extract depth information of the relevant scene. Until recently, mostly correlation-based methods have been used to solve the correspondence problem. However, the performance of correlation-based methods degrades significantly when there is a change in illumination between the two images of the stereo pair. Recent studies indicate MI to be a more robust stereo matching metric for images affected by such radiometric distortions. In this short correspondence paper, we compare the performances of MI and correlation-based metrics for different types of illumination changes between stereo images. MI, as a statistical metric, is computationally more expensive. We propose a wavelet-based hierarchical technique to counter the increase in computational cost and show its effectiveness in stereo matching.

Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain)

The Ayora region, situated about 60 km southwest of the city of Valencia/Spain, was chosen to demonstrate pathways of characterizing fire events and post-fire succession in Mediterranean ecosystems using multi-temporal satellite imagery. A corresponding time series of 6 Landsat MSS, 13 Landsat-5 TM and 1 Landsat-7 ETM + images, covering the period 1975—2000, was processed to account for geometric and radiometric distortions as well as sensor calibration. Spectral Mixture Analysis was applied to derive estimates of photosynthetic active green vegetation cover as a primary indicator. A combination of pixel-based linear trend analysis and diachronic thresholding was employed to procure a fire perimeter data base and characterize post-fire dynamics based on a temporally stratified trend analysis. The results were integrated with auxiliary information to evaluate driving factors and further interpreted in relation to field-based information on plant communities and post-fire succession patterns. Reflecting typical recovery phases with an initial establishment of grasses and herbaceous species, followed by a gradual development of a shrub layer and the subsequent colonization of tree individuals, temporal trajectories could be described by exponential functions and were related to plot-based botanical information.

Automated delineation of dry and melt snow zones in Antarctica using active and passive microwave observations from space

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated

A Global Quality Measurement of Pan-Sharpened Multispectral Imagery

This letter focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) observations. A new quality index suitable for MS imagery having four spectral bands is defined from the theory of hypercomplex numbers, or quaternions. Both spectral and radiometric distortion measurements are encapsulated in a unique measurement, simultaneously accounting for local mean bias, changes in contrast, and loss of correlation of individual bands, together with spectral distortion. Results are presented and discussed on very high-resolution QuickBird data, through comparisons between state-of-the-art and advanced MS+Pan merge algorithms.

The use of microwave SAR images for forest decline monitoring in mountainous area

In this paper, an attempt to use microwave images for the monitoring of forest deterioration in the Sudety Mountains has been made. For Synthetic Aperture Radar (SAR) images acquired over mountainous area the removal of geometric and radiometric distortions should precede a thematic interpretation. In order to control the influence of digital terrain model on the accuracy of geocoding, a method of fast and precise rectification of SAR images to Polish topographic maps was elaborated. The dependence of the corrected and calibrated signal on the local incidence angle for different land cover forms was investigated. The classification aimed on a discrimination of forest from non forest area has been performed. The achieved accuracy depends on the choice of multi-temporal microwave data according to the varying moisture conditions.

RADIOMETRIC IMAGE CORRECTION WITH AUTOMATIC MODEL SELECTION

Computer vision is a powerful tool for intelligent sensor development. However, noise in CCD cameras leads to significant radiometric distortions. Therefore, radiometric image correction is a critical operation, specially when physics-based models are applied for image processing, as is the case in many industrial applications. All known radiometric correction methods assume that noise characteristics remain stable over time. In this paper, a new radiometric correction method is proposed to account for non static noise effects. The method decomposes radiometric distortion into multiplicative and additive errors, whose optimal models are computed with a new extension to the Generalized Cross-Validation Criterion.

A simple method to account for topography in the radiometric correction of radar imagery

This article presents a method to study and correct radiometric distortions caused by topography in SAR images. The method is easy to implement and requires neither sophisticated software nor code-level programming. It also considers the case of a flat surface having an elevation different from the one for which calibration parameters were derived. An ortho-image of the slant range distance is used with a Digital Elevation Model to generate images of the local incident angle along the range and azimuth directions. The method compensates for variations in the terrain area of each pixel and for the angular dependence of backscatter, allowing the choice of either an empirical or semi-empirical scattering model. The method is applied to high-resolution C-SAR subsets of an agricultural area in the Central Cordillera of Costa Rica. The removal of topographic features appears excellent for local incident angles up to 80°, but small-scale structures have pronounced effects on the radar return for higher local incident angles and are not adequately corrected.

Fast and Robust Stereo Matching Algorithms for Mining Automation

The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

3D area matching with arbitrary multiview geometry 1 1Work supported in part by the ACTS Project ‘PANORAMA’, Proj. No. AC092, and in part by the CNR Strategic Project “Conoscenza per immagini; un’applicazione ai beni culturali” (“Knowledge acquisition from images: an application to cultural heritage”).

In this article we present a general and robust approach to the problem of close-range 3D reconstruction of objects from stereo correspondence of luminance patches. The method is largely independent on the camera geometry, and can employ an arbitrary number of CCD cameras. The robustness of the approach is due to the physicality of the matching process, which is performed in the 3D space. In fact, both 3D location and local orientation of the surface patches are estimated, so that the geometric distortion can be accounted for. The method takes into account the viewer-dependent radiometric distortion as well. The method has been implemented with a calibrated set of three standard TV-resolution CCD cameras. Experiments on a variety of real scenes have been conducted with satisfactory results. Quantitative and qualitative results are reported.