Highly efficient target detection algorithms in hyperspectral remote sensing technology, particularly for the long range detection of very low observable objects which exhibit extremely small detection cross sections, are in great demand. This is more so for a near or real time application. This paper is concerned with global anomaly detections (GAD), and conventional methods to achieve better detection using multiple approach fusion (MAF), which fuses detection outputs from various detectors using either logical operators, or, via a model based estimation of the joint detection statistics from all detectors, is found to be not good enough. This work emphasises the need to integrate a more comprehensive background modelling into the GAD to develop a robust anomaly detector (AD). Then, the detection output from this detector is fused with other detectors via MAF for a further improvement of detection performance. The MUF2 algorithm is formulated exactly using this 2-level fusion mechanism, in which m ixture modelling and spectral u nmixing f usion have been employed. The significance of background modelling in GAD has been highlighted in this work using real data. The result has shown a factor of 2-5 reduction in detection performance when a very small amount of target pixels (~0.1%) is misclassified as background. This is because anomalies are defined with reference to a model of the background, and subsequently two new background classification techniques have been proposed in this work. The effectiveness of the MUF2 has been assessed using three representative data sets which contain various different types of targets, ranging from vehicles to small plates embedded in backgrounds with various degrees of homogeneity. The performance of MUF2 has been shown to be more superior than the conventional GAD frequently in orders of magnitude, regardless of the background homogeneity and target types. The current version of the MUF2 is run under Matlab and it takes ~2 minutes to process a 20K pixel imagery. This work forms part of the research programme supported by the EMRS DTC established by the UK MOD.