Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation

In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.

ArcGIS-mapping to sustainable wastewater management in Greece: siting artificial wetlands systems in a biosystem

<p>In this work, the ArcGIS technology combines analogue and digital geospatial data to derive multiple resolution meshes with a triangulated irregular networks (TINs) approach that serves to integrate the geospatial data such as surface topography, hydro graphic features and land surface characteristics into an adaptive representation of a basin biosystem. The ArcGIS model that has been developed is applied at the municipal level to a small remote settlement with less than 2000 people in Northern Greece. The aim was a site assessment for constructing an artificial wetland (ATW) system as a viable solution to the wastewater management problem and protection of biosystems. This study demonstrates that there are discrepancies in Greece between the existing open geospatial data and on the basis of the results from our study we can conclude that this combination of local maps and geographic information in ArcGIS with a TIN approach increases our knowledge of the physical terrain. It accordingly facilitates the analysis and implementation of action plans by selecting suitable sites for construction of ATW systems in small remote settlements. We moreover discuss problems regarding spatial data quality and scale and provide suggestions for improvement while the desktop classification steps can be easily reproduced for other data-similar countries.</p>