Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search

When designing multispectral imaging systems for classifying different spectra it is necessary to choose a small number of filters from a set with several hundred different ones. Tackling this problem by full search leads to a tremendous number of possibilities to check and is NP-hard. In this paper we introduce a novel fast binary search for optimal filter selection that guarantees a minimum distance metric between the different spectra to classify. In our experiments, this procedure reaches the same optimal solution as with full search at much lower complexity. The desired number of filters influences the full search in factorial order while the fast binary search stays constant. Thus, fast binary search allows to find the optimal solution of all combinations in an adequate amount of time and avoids prevailing heuristics. Moreover, our fast binary search algorithm outperforms other filter selection techniques in terms of misclassified spectra in a real-world classification problem

Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering

Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major disadvantages like not being able to record videos. Therefore, multispectral cameras with wide-band filters are used, which are much cheaper and are often able to capture videos. However, using multispectral cameras requires an additional reconstruction step to yield spectral information. Usually, this reconstruction step has to be done in the presence of imaging noise, which degrades the reconstructed spectra severely. Typically, same or similar pixels are found across the image with the advantage of having independent noise. In contrast to state-of-the-art spectral reconstruction methods which only exploit neighboring pixels by block-based processing, this paper introduces non-local filtering in spectral reconstruction. First, a block-matching procedure finds similar non-local multispectral blocks. Thereafter, the hyperspectral pixels are reconstructed by filtering the matched multispectral pixels collaboratively using a reconstruction Wiener filter. The proposed novel procedure even works under very strong noise. The method is able to lower the spectral angle up to 18% and increase the peak signal-to-noise-ratio up to 1.1dB in noisy scenarios compared to state-of-the-art methods. Moreover, the visual results are much more appealing

Spatio-spectral Image Reconstruction Using Non-local Filtering

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions

Structure-Preserving Spectral Reflectance Estimation using Guided Filtering

Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost for acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique which preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out-of-the-box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels

Drought, Heat, and the Carbon Cycle: a Review

Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.publishedVersio

Tropical Cyclogenesis Sensitivity to Environmental Parameters in Radiative-Convective Equilibrium

In this study, the relationship between the likelihood of tropical cyclogenesis and external environmental forcings is explored in the simplest idealized modelling framework possible: radiative-convective equilibrium on a doubly periodic f-plane. In such an environment, control of the equilibrium environmental sounding is reduced to three parameters: the sea-surface temperature, the Coriolis parameter, and the imposed background surface wind speed. Cloud-resolving mesoscale model simulations are used to generate environments of radiative-convective equilibrium determined by these three factors. The favourability of these environments for tropical cyclogenesis is measured in three ways: in terms of the maximum potential intensity (MPI) of the sounding, based on the thermodynamic theory of Emanuel; in terms of the ‘genesis potential’ determined by an empirical genesis parameter; and in terms of the propensity of weak initial vortices in these environments to form into tropical cyclones. The simulated environments of radiative—convective equilibrium with no vertical wind shear are found to be very favourable for tropical cyclogenesis. Weak initial vortices always transition to a tropical cyclone, even for rather low sea-surface temperatures. However, the time required for these vortices to make the transition from a weak, mid-level vortex to a rapidly developing tropical cyclone decreases as the MPI increases, indicating the importance of MPI in enhancing the frequency of cyclogenesis. The relationship between this ‘time to genesis’ and the thermodynamic parameters is explored. The time to genesis is found to be very highly (negatively) correlated to MPI, with little or no relationship to convective instability, Coriolis parameter, mid-level humidity, or the empirical genesis parameter. In some cases, tropical cyclones are found to form spontaneously from random convection. This formation is due to a cooperative interaction between large-scale moisture, long-wave radiation, and locally enhanced sea-surface fluxes, similar to the ‘aggregation’ of convection found in previous studies.National Science Foundation (U.S.) (Grant ATM-0432067

Implementation and management of teletraining: proceedings of a WIK-Workshop held in Brussels, november 30, 1993

Includes 6 articlesSIGLEAvailable from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel W 208 (123) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Implementation and management of teletraining

SIGLEAvailable from UuStB Koeln(38)-980106145 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman