Vector attribute profiles for hyperspectral image classification

International audienceMorphological attribute profiles are among the most prominent spectral-spatial pixel description methods. They are efficient, effective and highly customizable multi-scale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general, and hyperspectral images in particular, has been so far conducted using the marginal strategy, i.e. by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector ordering relation that leads to the computation of a single max-and min-tree per hyperspectral dataset, from which attribute profiles can then be computed as usual. We explore known vector ordering relations for constructing such max-trees and subsequently vector attribute profiles, and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common datasets, where the proposed approach outperforms the widely used marginal strategy

The EFIGI catalogue of 4458 nearby galaxies with morphology II. Statistical properties along the Hubble sequence

The EFIGI catalogue of 4458 galaxies provides a reference database of the morphological properties of nearby galaxies, with 16 shape attributes describing their various dynamical components, their texture and environment, and with a dense sampling of all Hubble types. This catalogue allows us to derive a quantitative description of the Hubble Sequence in terms of the specific morphological features of the various types. The variations of the EFIGI morphological attributes with type confirm that the visual Hubble sequence is a decreasing sequence of bulge-to-total ratio and an increasing sequence of disk contribution to the total flux. There is nevertheless a large dispersion of approximately 5 types for a given bulge-to-total ratio, due to the fact that the Hubble sequence is primarily based on the strength and pitch angle of the spiral arms, independently from the bulge-to-total ratio. The grand spiral design is also related to a steep decrease in visible dust from types Sb to Sbc-Sc. In contrast, the scattered and giant HII regions show different strength variation patterns; hence, they do not appear to directly participate in the establishment of the Hubble sequence. The distortions from a symmetric profile also incidentally increase along the sequence. Bars and inner rings are frequent and occur in 41% and 25% of disk galaxies resp. Outer rings are twice less frequent than inner rings, and outer pseudo-rings occur in 11% of barred galaxies. Finally, we find a smooth decrease in mean surface brightness and intrinsic size along the Hubble sequence. The largest galaxies are cD, Ellipticals and Sab-Sbc spirals, whereas Sd and later spirals are nearly twice smaller. S0 are intermediate in size, and Im, cE and dE are confirmed as small objects. Dwarf spiral galaxies of type Sa to Scd are rare in the EFIGI catalogue, we only find 2 such objects.Comment: Accepted for publication in Astronomy and Astrophysics, 22 pages, 10 tables, 19 colour figures. Data available at http://www.efigi.or

Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images

The high resolution of the hyperspectral remote sensing images available allows the detailed analysis of even small spatial structures. As a consequence, the study of techniques to efficiently extract spatial information is a very active realm. In this paper, we propose a novel denoising wavelet-based profile for the extraction of spatial information that does not require parameters fixed by the user. Over each band obtained by a wavelet-based feature extraction technique, a denoising profile (DP) is built through the recursive application of discrete wavelet transforms followed by a thresholding process. Each component of the DP consists of features reconstructed by recursively applying inverse wavelet transforms to the thresholded coefficients. Several thresholding methods are explored. In order to show the effectiveness of the extended DP (EDP), we propose a classification scheme based on the computation of the EDP and supervised classification by extreme learning machine. The obtained results are compared to other state-of-the-art methods based on profiles in the literature. An additional study of behavior in the presence of added noise is also performed showing the high reliability of the EDP proposedThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional under Grants GRC2014/008 and ED431C 2018/2019 and the Ministerio de Economía y Empresa, Gobierno de España under Grant TIN2016-76373-P. Both are cofunded by the European Regional Development FundS

Automatic identification of sites prone to topographic seismic amplification effects by the current seismic codes

Current seismic codes provide proxies to estimate seismic amplification effects expected in correspondence of some morphological features. To make possible any empirical validation of these proxies, these features must be univocally identified on the basis of an automatic procedure. To this purpose, based on geomorphological considerations, a GIS-based numerical approach has been developed. The results of a morphometric analysis allowed the correct identification and mapping of the landforms of concern, at a detail corresponding to the resolution of the available digital elevation model (DEM). Some case-studies are provided to show the feasibility of the proposed approach. © 2023 The Author

Form and function in hillslope hydrology : in situ imaging and characterization of flow-relevant structures

Thanks to Elly Karle and the Engler-BunteInstitute, KIT, for the IC measurements of bromide. We are grateful to Selina Baldauf, Marcel Delock, Razije Fiden, Barbara Herbstritt, Lisei Köhn, Jonas Lanz, Francois Nyobeu, Marvin Reich and Begona Lorente Sistiaga for their support in the lab and during fieldwork, as well as Markus Morgner and Jean Francois Iffly for technical support and Britta Kattenstroth for hydrometeorological data acquisition. Laurent Pfister and Jean-Francois Iffly from the Luxembourg Institute of Science and Technology (LIST) are acknowledged for organizing the permissions for the experiments. Moreover, we thank Markus Weiler (University of Freiburg) for his strong support during the planning of the hillslope experiment and the preparation of the manuscript. This study is part of the DFG-funded CAOS project “From Catchments as Organised Systems to Models based on Dynamic Functional Units” (FOR 1598). The manuscript was substantially improved based on the critical and constructive comments of the anonymous reviewers, Christian Stamm and Alexander Zimmermann, and the editor Ross Woods during the open review process, which is highly appreciated.Peer reviewedPublisher PD

Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics.Rannís; Rannsóknarnámssjóður / The Icelandic Research Fund for Graduate Students.PostPrin