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

Morphological Principal Component Analysis for Hyperspectral Image Analysis

International audienceThis paper deals with a problem of dimensionality reduction for hyperspectral images using the principal component analysis. Hyper-spectral image reduction is improved by adding structural/spatial information to the spectral information, by means of mathematical morphology tools. Then it can be useful in supervised classification for instance. The key element of the approach is the computation of a covariance matrix which integrates simultaneously both spatial and spectral information. Thanks to these new covariance matrices, new features can be extracted. To prove the efficiency of these new features we have conducted an extended study showing the interest of the structural/spatial information

Multi-scale texture segmentation of synthetic aperture radar images

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Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Fuzzy-GIS development of land evaluation system for agricultural production in North West Libya

The continuing deterioration of land and water resources occurring in several regions of the world is partly as a result of the mismatch between land suitability or capability and land use. Failure to achieve a perfect match between land capability and use can be particularly problematic for agricultural production because cultivating the wrong crops on wrong soils can only result in poor yields and its associated financial and other losses. There is therefore, a pressing need for effective land evaluation through better matching of land characteristics with land use to achieve optimal utilisation of available land resources for sustainable agricultural production. As far as agriculture is concerned such an exercise will result in defining which part of an area is suitable for particular crops, based on the available land resources and other production inputs, and which parts are better left for other uses. In this study, a land evaluation system for predicting the physical suitability of land for key crops, namely Wheat, Barley and Olive in the north west of Libya was developed based on matching land use requirement for these crops with the available land resources in the area. It involved a modelling strategy based on Boolean and Fuzzy logic sets, implemented within a Geographic Information System (GIS) environment. While the Boolean method assumes that the attributes of a given soil type are known with certainty and the boundaries between soil types are clearly defined, Fuzzy logic can be used to accommodate uncertainties in the available knowledge on these attributes through the use of membership functions. The GIS-based models developed comprise four layers; namely, soil, climate, slope and erosion hazard all of which have been shown directly influence land suitability for agricultural production. This resulted in the classification of the soil into 4 suitability classes, i.e. high suitability, moderate suitability, marginal suitability and not suitable. The results show that for Barley for example 52% of the soil in the north western Libya is highly suitable using Fuzzy approach while the corresponding figure for the Boolean is 62%. The two approaches were compared on cell by cell basis using map agreement. The comparison shows that there were reasonable agreements in evaluations by the two approaches for barley, wheat and olive of 51%, 46% and 56% respectively