Abstract: An automatic approach to detect and classify targets in high-resolution broad-area remote sensing images is explored, which relies on detecting statistical signatures of targets, in terms of a set of  biologically-inspired lowlevel visual features. The broad-area remote sensing images were first cut into small image chips with slide window, which were analyzed in two complementary ways: attention/saliency analysis exploits local features and their interactions across space, while gist analysis focuses on global non-spatial features and their statistics. Both saliency and gist feature sets were used to classify each chip as containing target or not, through using a support vector machine. The proposed algorithm outperformed the state-of-the-art HMAX algorithm in the experiments and thus can be used to reliably and effectively detect highly variable target objects in large scale remote sensing image datasets.