Multi-angular Images Research Articles

Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system.A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

Bathymetric Retrieval Selectively Using Multiangular High-Spatial-Resolution Satellite Imagery

This article introduces multiangular imagery into physics-based bathymetry in order to compensate for the shortage of bathymetric spectral bands caused by the low spectral resolution of current high-spatial-resolution satellite multispectral imagery. The focus is to propose a selective bathymetric retrieval method to eliminate the negative effect of nonoptimal image data on depth retrieval in multiangular imagery-based bathymetry. The elimination of the negative effect is implemented by excluding nonoptimal pixels in every individual image from bathymetric retrieval. An empirical criterion is designed for the determination of nonoptimal pixels. The proposed method can use multiangular image data selectively, avoiding situations where bathymetric retrieval results from the whole multiangular imagery are poorer than that from a part of the individual images. The method was tested in two typical areas within the Xisha (Paracel) Islands of the South China Sea using two-angle WorldView-2 multispectral images. The test showed that the derived depths of the method (i.e., depths derived from the selective image data) provided a better fit to the validation depths than those from the entirety of both images. The underestimation of depths derived from the entirety of both images was also improved to some extent.

The Research on Integrated Production of Large Scale 3D Products Based on Mass Multi-angular Images

Aim at the problems of high cost and low efficiency of traditional large scale 3D (three-dimensional) products acquisition methods, this paper provides an integrated production of large scale 3D products based on massive multi-angular Images. Firstly, multi-angular images were obtained according to oblique photogrammetric technique. After the operation of aerial triangulation and 3D construction, the real 3D model is achieved. On these base, integrated production of large 3D products were done. Through the statistical analysis of 3D products accuracy, its precision completely meets the requirements of large-scale data specification, which provides a technical guarantee for rapid and efficient data revision of basic geographic information.

A Method for Real-Time Patient Positioning Verification in Radiotherapy Using Multi-Angular Images with Conventional Video Cameras

Background: Accurate patient positioning and the corresponding position verification play a key role in precision radiotherapy. The conventional position verification techniques, such as cone-beam computed tomography (CBCT), used in radiotherapy deliver additional radiation doses to patients; thus some complications may occur in the surrounding normal tissue. Additionally, the present registration and fusion algorithms require a long time to verify and monitor the patient's position in real-time. Method: A new simple method using three conventional video cameras from different directions for observing the changes in the patient's position and taking the multiangular surface images of the patient without delivering additional doses of radiation is proposed in this paper. Real-time patient position verification could be achieved through this Oriented FAST and Rotated BRIEF (ORB) algorithm modified by us. Results: A plastic patient model immobilized with thermoplastic shell on a simulation couch was used to verify the accuracy and feasibility of our method. Testing results showed that the position and angle offset errors identified with our system were less than 1 mm and 1°, respectively. Moreover, the modified ORB algorithm was fast enough for real-time images comparison. Conclusion: This proposed method shows a great potential for application in verifying and monitoring the patient's position in real time during radiotherapy.

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40° and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CIred-edge), Green band Chlorophyll Index (CIgreen), Modified Normalized Difference Vegetation Index with a blue band (mNDblue) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CIgreen for LNC from a VZA of -60° (R2 = 0.71, RMSE = 0.34%) and PNC from a VZA of -40° (R2 = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0° image, the accuracy of PNC estimation was substantially improved (CIred-edge: R2 = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based multispectral camera for better estimation of nitrogen concentration in wheat leaves or plants. The oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoring.

Determination of Volcanic Plume Three-Dimensional Structure Using CHRIS/PROBA-1 Multiangular Imagery: Case Study of Mount Etna

The recent development of Earth observation satellites with multiangular capabilities and enhanced spectral resolution has led to preliminary attempts at determining the height of atmospheric scatterers, in particular, of top-cloud heights and smoke plumes originating from forest fires. Inspired by these previous studies, the present work presents an original methodology for the determination of the three-dimensional distribution of high-contrast atmospheric aerosols using multiangular images. The method starts with the approximately known geometry of image acquisition and a set of tie points and uses a linearized and regularized functional model to obtain the position of atmospheric scatterers identified by means of a semiassisted procedure on two or more images. A subsequent application to a CHRIS/PROBA-1 scene of Mount Etna following its eruption on June 14, 2014, allows determining the volcanic plume three-dimensional structure with a precision in the 100–200 m level.

Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

Considering that satellites rarely acquire data from the exact nadir direction, angular effect normalization needs to be conducted as an important preprocessing step to correct reflectance observations from off-nadir directions into the nadir direction. Kernel-based bidirectional reflectance distribution function models have been employed for angular effect correction. The kernels used in the model are often predetermined and fixed for an entire image. However, the fixed kernels are unable to accommodate the various reflective characteristics of different ground cover types present in the imaged area. In this paper, we propose a kernel learning procedure that enables the flexible selection of kernels for different land cover types within a scene. The kernels are selected from kernel dictionaries that contain multiple candidate kernels. The selection is conducted on the superpixel level instead of the pixel level in order to reduce within-class variation and overcome the overfitting problem. Experiments are conducted on multiangular images acquired by the Sentinel-2A satellite over a rural area in southeastern Australia. Cross-validation results show that the proposed method is able to adaptively select appropriate kernels for different land cover types, leading to an improved performance for image normalization.

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image classification; 4) multisensor image fusion exploiting physically-based feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.

Multi‐angular ground‐based SAR system for soil surface roughness characterisation

Developped in the Institut of Electronics and Telecommunication of Rennes (IETR), a Ground Based (GB) SAR system is proposed for soil surface roughness discrimination at C-band. By using this GB-SAR system, the multi-angular SAR measurement is realized at different resolution cell sizes. The specific levels of surface roughness are set deliberately to analyze the roughness contribution on multi-angular SAR signature in a controlled experimental condition. Based on the GB-SAR measurement, the effect of incidence angle on SAR sensitivity is studied under two resolution cell sizes for retrieving soil surface roughness. The results show that, by using our GB-SAR system, high incidence angle configuration (e.g. > 40 •) is optimal to discriminate surface roughness, which is more significant at small resolution cell size (e.g. 30 cm). This study indicates the effectivity of GB-SAR system to validate the SAR backscattering mechanisms and will be helpful for future spatial missions [1]. Introduction: With high spatial and temporal resolution, the remote sensing techniques play an important role in monitoring the soil status. Compared with optical remote sensing, the synthetic aperture radar (SAR) in independent of weather and light constraint, owing to the active microwave generation and the penetration ability of microwave. Nevertheless, the mechanisms of SAR backscattering from bare soils are suffered from the coherent superposition of surface roughness contribution with soil moisture. Thus, discrimination of surface roughness effect on SAR signature is crucial for further soil monitoring. Recently, the multi-angular SAR measurements increase the observing dimension [2], making it possible to improve the robustness of soil parameter retrievals. However, the temporal interval of spaceborne multi-angular image acquisitions induces several problems in the multi-angular SAR study: i) the soil characteristics may vary during the multi-angular measurements; ii) it is challenged to accomplish the consistent ground truth measurements over large areas simultaneously with satellite pass. To solve the aforementioned issues, this study proposes a Ground Based (GB) SAR system for bare soil monitoring. This kind of GB-SAR system was also developed for terrain mapping [3], vegetation monitoring [4] and snow pack imaging [5]. However, our study concentrates on the bare soil characterization under multi-angular configuration and the influence of resolution cell size on the multi-angular SAR sensitivity. GB-SAR system for soil monitoring: Our GB-SAR system is mainly comprised of three components: i) A vector network analyzer; ii) Four wide band horn antennas (2-12 GHz); iii) A three-meter long metal rail with an accurate positioning motor. This GB-SAR system is capable of measuring the backscattering matrix of targets under controlled experimental condition. Repeatable measurements can be realized by the GB-SAR system as the motion of the platform is controlled accurately, which is essential for multi-angular SAR measurements. Furthermore, different soil status can be set deliberately in the scene to analyze the induced response in SAR signature. The SAR back projection algorithm [6] makes possible to obtain the absolute backscattering coefficients through several calibration procedures: i) The antenna patterns are compensated along both azimuth and range axis; ii) The methodology proposed by [7] is used to accomplish the polarimetric calibration; iii) The radiometric calibration is realized by comparing the measured RCS (Radar Cross Section) of a canonical reference target with its theoretical RCS value.

Semisupervised Manifold Alignment of Multimodal Remote Sensing Images

We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor and multiangular images is available. In these situations, images should ideally be spatially coregistred, corrected and compensated for differences in the image domains. Such procedures require the interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps and foldings of the image distributions (or manifolds). The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds, and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike, and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains.

A DECISION LEVEL FUSION METHOD FOR OBJECT RECOGNITION USING MULTI-ANGULAR IMAGERY

Abstract. Spectral similarity and spatial adjacency between various kinds of objects, shadow and occluded areas behind high rise objects as well as complex relationships lead to object recognition difficulties and ambiguities in complex urban areas. Using new multi-angular satellite imagery, higher levels of analysis and developing a context aware system may improve object recognition results in these situations. In this paper, the capability of multi-angular satellite imagery is used in order to solve object recognition difficulties in complex urban areas based on decision level fusion of Object Based Image Analysis (OBIA). The proposed methodology has two main stages. In the first stage, object based image analysis is performed independently on each of the multi-angular images. Then, in the second stage, the initial classified regions of each individual multi-angular image are fused through a decision level fusion based on the definition of scene context. Evaluation of the capabilities of the proposed methodology is performed on multi-angular WorldView-2 satellite imagery over Rio de Janeiro (Brazil).The obtained results represent several advantages of multi-angular imagery with respect to a single shot dataset. Together with the capabilities of the proposed decision level fusion method, most of the object recognition difficulties and ambiguities are decreased and the overall accuracy and the kappa values are improved.

Overview of the First SMOS Sea Surface Salinity Products. Part I: Quality Assessment for the Second Half of 2010

Multi-angular images of the brightness temperature ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ) of the Earth at 1.4 GHz are reconstructed from the Soil Moisture and Ocean Salinity (SMOS) satellite sensor data since end 2009. Sea surface salinity (SSS) products remote sensing from space is being attempted using these data over the world oceans. The quality of the first version of the European Space Agency operational Level 2 (L2) SSS swath products is assessed in this paper, using satellite/in situ SSS data match-ups that were collected over the second half of 2010. This database reveals that 95% of the SMOS L2 products show a global error standard deviation on the order of ~ 1.3 practical salinity scale. Simple spatiotemporal aggregation of the L2 products to generate monthly SSS maps at 1° ×1° spatial resolution reduces the error down to about 0.6 globally and 0.4 in the tropics for 90% of the data. Several major problems are, however, detected in the products. Systematically, SMOS SSS data are biased within a ~ 1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. Numerous world ocean areas are permanently or intermittently contaminated by radio-frequency interferences, particularly in the northern high latitudes and following Asia coastlines. Moreover, temporal drifts in the retrieved SSS fields are found with varying signatures in ascending and descending passes. In descending passes, a time-dependent strong latitudinal bias is found, with maximum amplitude reached at the end of the year. Errors in the forward modeling of the wind-induced emissivity and of the sea surface scattered galactic sources are as well identified, biasing the sss retrievals at high and low winds and when the galactic equator sources are reflected toward the sensor.

Fast Surface Height Determination Using Multi-Angular WorldView-2 Ortho Ready Urban Scenes

We present a novel method that automatically and accurately determines surface elevation in multi-angular WorldView-2 Ortho Ready Product panchromatic images without the need for detailed satellite ephemeris information. The solution expands our work on building height determination using fast template matching on GPUs, which was selected as one of the winners of the 2011 IEEE-DigitalGlobe Data Fusion Contest. The presented method automatically determines parallax in the multi-angular images and derives local object height from it. First, the method is demonstrated on calculating building heights which are validated against building heights acquired from public sources. The roof displacement analysis derived from the first results are then applied to produce a surface elevation for the full scene.

A Hybrid Approach for Building Extraction From Spaceborne Multi-Angular Optical Imagery

The advent of high resolution spaceborne images leads to the development of efficient detection of complex urban details with high precision. This urban land use study is focused on building extraction and height estimation from spaceborne optical imagery. The advantages of such methods include 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, a hybrid approach is proposed for efficient building extraction from optical multi-angular imagery, where a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. This approach is tested on ortho-rectified Level-2a multi-angular images of Rio de Janeiro from WorldView-2 sensor. Its performance is validated using a 3-fold cross validation strategy. The final results are presented as a building map and an approximate 3D model of buildings. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.

A nonlinear unmixing method in the infrared domain

This paper presents the physical principle of a new (to our knowledge) unmixing method to retrieve optical properties (reflectance and emissivity) and surface temperatures over a heterogeneous and a folded landscape using hyperspectral and multiangular airborne images acquired with high spatial resolution. In fact, over such a complex scene, the linear mixing model of the reflectance commonly used in the reflective domain is no longer valid in the IR range for the two following reasons: multiple reflections due to the three-dimensional (3D) structure and the radiative phenomenon introduced by the temperature by way of the black body law. Thus, to solve this nonlinear unmixing problem, a new physical model of aggregation is used. Our model requires as inputs knowledge of the 3D scene structure and the spatial contribution of each material in the scene. Each elementary scene element is characterized by its optical properties, and its temperature, spectral, and multiangular acquisitions are required. This paper focuses only on the theoretical feasibility of such a method. In addition, an analysis is conducted evaluating the impact of the misregistration between the radiometric image and its digital terrain model, estimating a threshold of the relative importance of every elementary material to retrieve its corresponding optical properties and temperature. The results show that the 3D geometry must be accurately known (accuracy of 1 m for a spatial resolution of 20 m), and the relative contribution of material in the mixed area must be above 15% to retrieve its surface temperature with an accuracy better than 1 K. So, this method is applied on three different landscapes (heterogeneous flat surface, V shape, and urban canyon), and the results exhibit performances better than 1% for optical properties and 1 K for surface temperatures.

Atmospheric correction of PROBA/CHRIS data in an urban environment

The Compact High Resolution Imaging Spectrometer (CHRIS) is an imaging spectrometer onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) satellite. However, it has been shown that CHRIS presents some miscalibration trends over the spectral region covered. This paper reports a practical procedure for the atmospheric correction of CHRIS images based on field recalibration in an urban environment. In the first stage, the spectra of surface targets are measured and used to simulate the spectral radiance at the top of the atmosphere (TOA) for each channel and to determine the recalibration coefficients of the CHRIS images. In the second stage, two methods for atmospheric correction are examined: the radiative transfer model (RTM) and the improved dark-object subtraction (IDOS) method. For comparison purposes, the empirical line method (ELM) is also evaluated. The accuracy assessment shows that the RTM with the Moderate Resolution Transmittance (MODTRAN) code provides the most accurate atmospheric correction for the multiangular CHRIS images when using the proposed procedure.

Accurate LAI retrieval method based on PROBA/CHRIS data

Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.

Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model

Given the hyperspectral-oriented waveband configuration of multiangular CHRIS/Proba imagery, the scope of its application could widen if the present 18-m resolution would be improved. The multiangular images of CHRIS could be used as input for superresolution (SR) image reconstruction. A critical procedure in SR is an accurate registration of the low-resolution images. Conventional methods based on affine transformation may not be effective given the local geometric distortion in high off-nadir angular images. This paper examines the use of a nonrigid transform to improve the result of a nonuniform interpolation and deconvolution SR method. A scale-invariant feature transform is used to collect control points (CPs). To ensure the quality of CPs, a rigorous screening procedure is designed: 1) an ambiguity test; 2) the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -estimator sample consensus method; and 3) an iterative method using statistical characteristics of the distribution of random errors. A thin-plate spline (TPS) nonrigid transform is then used for the registration. The proposed registration method is examined with a Delaunay triangulation-based nonuniform interpolation and reconstruction SR method. Our results show that the TPS nonrigid transform allows accurate registration of angular images. SR results obtained from simulated LR images are evaluated using three quantitative measures, namely, relative mean-square error, structural similarity, and edge stability. Compared to the SR methods that use an affine transform, our proposed method performs better with all three evaluation measures. With a higher level of spatial detail, SR-enhanced CHRIS images might be more effective than the original data in various applications.

LAI retrieval from multiangular image classification and inversion of a ray tracing model

A non-conventional approach for the estimation of leaf area index (LAI) and leaf angle distribution (LAD), based on the use of information contained in multiangular images and the inversion of a canopy ray tracing model, is proposed in this work. As an alternative to the use of overall image reflectance data, the image fraction components, i.e. sunlit and shaded leaves and soil, are obtained by supervised classification of ground-based multiangular images acquired using an inexpensive colour infrared camera, the Dycam ADC. These data are used for the inversion of a numerical model of a vegetation canopy in which the latter is described as composed of randomly distributed disks (leaves). The model was developed using the POV-ray ray tracer. Model inversion is carried out using the look-up-table approach. The proposed methodology was tested using an extensive data set gathered on the potato crop during experimental trials carried out at Viterbo (Italy) for 3 years. The results show that LAI was successfully estimated with a RMSE varying from 0.29 to 0.75 in the different years. The potential sources of error in both estimated and measured LAI values are extensively discussed.

Use of HRSC-A for sampling bidirectional reflectance

This paper describes a method for sampling bidirectional reflectance information from multiangular airborne images. The method uses high resolution surface models to determinate the location of the imaged point on the ground and the orientation of the measured surface fragment. Since natural surfaces scatter incident radiation anisotropically, viewing and illumination conditions play a critical role in the interpretation of remotely sensed images. Thus, directionally defined reflectance data are needed for the modelling and correction of bidirectional effects on airborne optical images. Two test sites were imaged with a wide range of viewing azimuth angles at two different times. A high resolution HRSC-A stereo camera was used for image acquisition. Algorithms were implemented to reconstruct the image acquisition and retrieve the image samples from the HRSC-A image data. Combined with GPS and INS data, automatically derived high resolution digital surface models, including vegetation canopies, houses, etc., were used to determine the viewing and illumination geometry on the target surface. The brightness of a sample point was recorded as a measure for reflectance. A large number of directionally defined samples and a wide angular range of sample geometry were obtained. The images were first classified. Sampled reflectance data were verified by investigating the bidirectional reflectance of five agricultural and forest targets. Errors affecting the data quality, such as angular uncertainty, were studied. The multiangular image data, the developed sampling methods and the obtained bidirectional dataset proved to be applicable in investigations of bidirectional reflectance effects of natural targets. Airborne imagery combined with high resolution digital surface models permit extensive investigation of the bidirectional reflectance of a wide range of natural objects and large habitats.