By constructing a model combining affine-invariant discrete hashing （AIDH） and confidential random field （CRF） ， the object detection of remote sensing image is achieved. Firstly， the remote sensing image is reconstructed by superpixel segmentation， and the undirected graph structure with superpixel block as vertex is constructed for CRF. Then， the superpixel block is used as the test sample for AIDH learning which is used as CRF unary potential function to generate the initial category label. Then， the pairwise potential function of CRF is constructed by using Potts model for label re-learning， while the object neighborhood information is smoothed and the missing area of object detection is resolved. Finally， the convex hull boundary method is used for generating minimum external rectangular frame as object detection result. Experimental results demonstrate that the proposed method achieves the tradeoff of accuracy and efficiency for objection detection tasks.