The identification of significant underlying data patterns such as image composition and spatial arrangements is fundamental in remote sensing tasks. Therefore, the development of an effective approach for information extraction is crucial to achieve this goal. Affinity propagation (AP) algorithm is a novel powerful technique with the ability of handling with unusual data, containing both categorical and numerical attributes. However, AP has some limitations related to the choice of initial preference parameter, occurrence of oscillations and processing of large data sets. This paper evaluates the clustering performance of AP algorithm taking into account the influence of preference parameter and damping factor. The study was conducted considering the AP algorithm, the adaptive AP and partition AP. According to the experiments, the choice of preference and damping greatly influences on the quality and the final number of clusters.
This paper investigates an alternative classification method that integrates class-based affinity propagation (CAP) clustering algorithm and maximum likelihood classifier (MLC) with the purpose of overcome the MLC limitations in the classification of high dimensionality data, and thus improve its accuracy. The new classifier was named CAP-MLC, and comprises two approaches, spectral feature selection and image classification. CAP clustering algorithm was used to perform the image dimensionality reduction and feature selection while the MLC was employed for image classification. The performance of MLC in terms of classification accuracy and processing time is determined as a function of the selection rate achieved in the CAP clustering stage. The performance of CAP-MLC has been evaluated and validated using two hyperspectral scenes from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and the Hyperspectral Digital Imagery Collection Experiment (HYDICE). Classification results show that CAP-MLC observed an enormous improvement in accuracy, reaching 94.15% and 96.47% respectively for AVIRIS and HYDICE if compared with MLC, which had 85.42% and 81.50%. These values obtained by CAP-MLC improved the MLC classification accuracy in 8.73% and 14.97% for these images. The results also show that CAP-MLC performed well, even for classes with limited training samples, surpassing the limitations of MLC.
This article deals with a proposition of symbology for large-scale topographic mapping. Emphasis was given to the case of the Municipal City of Maputo and public and private institutions that operate in urban mapping. Of the five available topographic maps, visual analysis was carried out, based on legibility and visibility criteria previously proposed by other authors. For each class of features, the graphic primitives used in each card were identified and, in the evaluated sequence, the possibility of maintaining the graphic primitive or modifying its representation in the proposed symbology. In many cases, semantic and geometric generalization was necessary to fit the symbology. The research showed that the library of symbols proposed in table form can be applied in the topographic mapping of urban areas, provided that the precepts of cartographic language and theories of graphic semiology are observed in order not to compromise the process of cartographic communication. A proposition of symbology for large-scale topographic mapping requires the cartographer to comply with the rules and specification criteria for maintaining logical consistency between topographic maps of subsequent scales, such as maps of 1:25.000 and smaller that follow the norms established by CENACARTA.
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