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This paper examines the use of Support Vector Machines (SVMs) in the context of Hyperspectral Remote Sensing, an imaging technique where hundreds of contiguous energy-bands are used to identify ground materials. The purpose of the study is to select a reduced set of features using an SVM-based algorithm whilst maintaining or improving the target detection accuracy. We use an existing algorithm - the SVM- Confident Margin (SVM-CM), to identify only the necessary spectral bands (features) to discriminate between military targets and backgrounds. A limited selection of bands not only improved computational performance but also sub-pixel detection accuracy. The results were evaluated through a multiple regression framework used for sub-pixel detection. An optimal 59 bands out of 128 was selected from SVM- CM for which all 12 targets were detected at a false- detection cost that was 270 times less than the all-band case. All testing were carried out on Multi-Sensor Trial data (MUST 2000) involving military targets.