Sediment facies classification of a sandy shoreline by means of airborne imaging spectroscopy

Airborne imaging spectroscopy data (AISA Eagle and HyMap) were applied to classify the sediments of a sandy beach in seven sand type classes. On the AISA‐Eagle data, several classification strategies were tried out and compared with each other. The best classification results were obtained applying a linear discriminant classifier (LDC) in combination with feature selection based on sequential floating forward search (SFFS). The statistical LDC was used in a multiple binary approach. In the first step, the original bands were used in the classification, but transformation of the bands to wavelet coefficients enhanced the accuracy obtained. The combination of LDC with SFFS resulted in an overall accuracy of 82% (using three wavelet coefficients). Replacing the LDC with the non‐statistical SAM algorithm reduced the overall accuracy to 74% (using all bands or wavelet coefficients). When applying LDC, the optimal number of bands/wavelet coefficients to be used was defined: using more than two bands or three wavelet coefficients did not result in a higher classification accuracy. Finally, the HyMap data, featuring 126 bands in the VNIR‐SWIR range, were used to demonstrate that the VNIR range outperforms the SWIR range for this application.     

Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation

In this technical note we present a new technique using mixed linear models for characterizing a mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation from multiyear satellite imagery. The main benefit of our approach is an ability to determine the statistical significance of each annual spectral change. Knowledge of the annual spectral change characteristics can then be used to statistically determine if a disturbance event has occurred, the timing of a given disturbance event, as well as to provide information for clustering fitted multitemporal reflectance curves (i.e. spectral trajectories) with a common shape. The spatial clustering of spectral trajectories provides insights into the nature of the disturbance and recovery imposed by infestation over a 14-year period.     

An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis

We present a simple image transform that optimally combines four image channels into a greyscale image for threshold-based cloud detection. These image channels, namely blue, green, red and near infrared, are present on many low Earth-orbit resource satellites. Applying a single threshold to a greyscale image is a computationally efficient method suitable for onboard implementation. We used heteroscedastic discriminant analysis (HDA), which is a generalization of the popular dimension-reducing linear discriminant analysis, to transform the image. Comparative tests between HDA, existing transforms from the remote-sensing literature (the haze optimized and D transforms), as well as the single red and blue image channels were conducted. Although thin clouds remain challenging for global threshold-based techniques, the HDA transform consistently gave the best average segmentation errors across the test dataset. This dataset consisted of 32 1 megapixel Quickbird and Landsat images. HDA has not previously been applied to remote-sensing data.     

Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.     

Review article Multisensor image fusion in remote sensing: Concepts,methods and applications

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.     

Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area,Wuda, Inner Mongolia,China

Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land‐cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach..     

ASTER‐derived emissivity and coal‐fire related surface temperature anomaly: a case study in Wuda,north China

Subsurface and surface coal fires form serious environmental, economic and safety problems in coal‐producing countries like China and India. Remote sensing offers the possibility of detecting and studying thermal anomalies due to coal fires. Emissivity plays an important role in determining the surface temperature of a body using remotely sensed data. In the present study an attempt is made to use satellite‐derived emissivity to estimate the surface temperature in Wuda, north China. With the use of multispectral thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (five bands in 8.125–11.65 µm region) in combination with a Temperature/Emissivity Separation (TES) algorithm, the anomalous pixels due to coal fires can be extracted from the background to achieve a qualitative study of coal fires. In the present study, during night‐time overpass of ASTER, satellite images have been recorded and simultaneous field measurements were collected. These field measurements were used to process the satellite thermal data and to validate the results obtained. Using the TES approach, satellite‐based temperature corresponded well with actual field measurements at selected locations.     

The Pythagoras number of real sum of squares polynomials and sum of square magnitudes of polynomials

In this paper, we conjecture a formula for the value of the Pythagoras number for real multivariate sum of squares polynomials as a function of the (total or coordinate) degree and the number of variables. The conjecture is based on the comparison between the number of parameters and the number of conditions for a corresponding low-rank representation. This is then numerically verified for a number of examples. Additionally, we discuss the Pythagoras number of (complex) multivariate Laurent polynomials that are sum of square magnitudes of polynomials on the $n$ -torus. For both types of polynomials, we also propose an algorithm to numerically compute the Pythagoras number and give some numerical illustrations.     

Compensatability and optimal compensation of systems with white parameters in the delta domain

Using the delta operator, the strengthened discrete-time optimal projection equations for optimal reduced-order compensation of systems with white stochastic parameters are formulated in the delta domain. The delta domain unifies discrete time and continuous time. Moreover, when formulated in this domain, the efficiency and numerical conditioning of algorithms improves when the sampling rate is high. Exploiting the unification, important theoretical results, algorithms and compensatability tests concerning finite and infinite horizon optimal compensation of systems with white stochastic parameters are carried over from discrete time to continuous time. Among others, we consider the finite-horizon time-varying compensation problem for systems with white stochastic parameters and the property mean-square compensatability (ms-compensatability) that determines whether a system with white stochastic parameters can be stabilised by means of a compensator. In continuous time, both of these appear to be new. This also holds for the associated numerical algorithms and tests to verify ms-compensatability. They are illustrated with three numerical examples that reveal several interesting theoretical and numerical issues. A fourth example illustrates the improvement of both the efficiency and numerical conditioning of the algorithms. This is of vital practical importance for digital control system design when the sampling rate is high.