Effects of mantle upwelling in a compressional setting: the Atlas Mountains of Morocco

We discuss the implications of a lithospheric model of the Moroccan Atlas Mountains based on topography, heat flow, gravity and geoid anomalies, taking into account the regional geology. The NW African cratonic lithosphere, some 160–180 km thick, thins to c. 80 km beneath the Atlas fold-thrust belts, in contrast with the shortening regime prevailing there since the early Cenozoic. This fact explains several geological and geophysical features as high topography with modest tectonic shortening, the occurrence of alkaline magmatism contemporaneous to compression, the absence of large crustal roots to support elevation, the scarce development of foreland basins, and a marked geoid high. The modelled lithosphere thinning is related to a thermal upwelling constrained between the Iberia–Africa convergent plate boundary and the Saharan craton.     

Hyperspectral image betweenness centrality clustering based adaptive spatial and spectral neighborhood approach for anomaly detection

Segmentation-based anomaly detectors proceeds to the clustering of the hyperspectral image as a first step. However, most of the well-known clustering methods cluster anomalous pixels as a part of the background. This paper presents a new hyperspectral image clustering approach based on the betweenness centrality measure. The proposed approach starts by the construction of an adaptive spatial and spectral neighborhood for each pixel. This neighborhood is based on the selection of the nearest spectral and spatial neighbors in multiple windows around each pixel to allow well-suited representation of the image features. In the next step, this neighborhood is clustered based on the edge betweenness measure algorithm that splits the image into regions sharing similar features. This approach (1) allows the reduction of intercluster relationship, (2) favors intracluster relations, and (3) preserves small clusters that can hold anomalous pixels. Experimental results show that the proposed approach is efficient for clustering and overcomes the state of the art approaches.