Abstract-Based on purely spectral-domain prior knowledge taken from the remote sensing (RS) literature, an original spectral (fuzzy) rule-based per-pixel classifier is proposed. Requiring no training and supervision to run, the proposed spectral rule-based system is suitable for the preliminary classification (primal sketch, in the Marr sense) of Landsat-5 Thematic Mapper and Landsat-7 Enhanced Thematic Mapper Plus images calibrated into planetary reflectance (albedo) and at-satellite temperature. The classification system consists of a modular hierarchical top-down processing structure, which is adaptive to image statistics, computationally efficient, and easy to modify, augment, or scale to other sensors' spectral properties, like those of the Advanced Spaceborne Thermal Emission and Reflection Radiometer and of the Satellite Pour l'Observation de la Terre (SPOT-4 and -5). As output, the proposed system detects a set of meaningful and reliable fuzzy spectral layers (strata) consistent (in terms of one-to-one or manyto-one relationships) with land cover classes found in levels I and II of the U.S. Geological Survey classification scheme. Although kernel spectral categories (e.g., strong vegetation) are detected without requiring any reference sample, their symbolic meaning is intermediate between those (low) of clusters and segments and those (high) of land cover classes (e.g., forest). This means that the application domain of the kernel spectral strata is by no means alternative to RS data clustering, image segmentation, and land cover classification. Rather, prior knowledge-based kernel spectral categories are naturally suitable for driving stratified application-specific classification, clustering, or segmentation of RS imagery that could involve training and supervision. The efficacy and robustness of the proposed rule-based system are tested in two operational RS image classification problems.Index Terms-Data clustering, fuzzy rule, fuzzy set (FS), generalization capability, image classification, image color analysis, image segmentation, one-class classifier, prior knowledge, remotely sensed imagery, spectral information, supervised and unsupervised learning from finite data.

No abstract

The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving.In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process.The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.

The southernmost European beech forests are located in the upper forest vegetation belt on Mount Etna volcano. Their standstructural patterns were analysed to assess the effects of the site-ecological factors and previous management practices on the forest structure. Five main structural-silvicultural types were identified among the main beech forest types: coppice, highmountain coppice (HMCo), high forest, coppice in conversion to high-forest and non-formal stand. A detailed standstructural analysis was carried out through measured dendrometric parameters and derived structural characters linked to both the horizontal and the vertical profiles. Plant regeneration processes were also assessed, and several biodiversity indicators were calculated. The collected data indicate a high variability of beech stand structures in relation to the heterogeneity of the site-ecological characteristics as well as to the effects of both natural and anthropic disturbance factors. The occurrence of particular stand structures along the altitude gradient on Mount Etna is evident. It is especially visible in the multi-stemmed HMCos in relation to the changing, and increasingly limiting, ecological factors, although at higher altitudes historical anthropic actions (felling) also have had an influence. Inside the Mediterranean area, these stands highlight their ecological marginality, in terms of both latitude and altitude, especially regarding current climate change processes.