Abstract: With the improvement of resolution,more and more useful information is contained in remote sensing images,which makes the processing of remote sensing data become more complex,and it is easy to cause the curse of dimensionality and the poor recognition effect.In view of this situation,a remote sensing targets detection approach(GJ-SRC) based on adaptive weighting feature dictionaries and joint sparse was proposed.Firstly,the Gabor transform is used to extract the features from the training images and the testing images.Then,the contribution weights of each eigenvalue in sparse representation are calculated,and the feature dictionary is constructed by the adaptive method,which makes the dictionary more discriminative.Finally,the common features of each category and the private features of a single image are extracted to form a joint dictionary,and the sparse representation of the test image is used for target recognition.In order to avoid the curse of dimensionality caused by the Gabor transform,the PCA method is used to reduce the dimension of the feature dictionary in order to reduce the computational cost.Experiments show that this method has better detection effect compared with the existing SRC method and remote sensing target detection method.

Key words: Gabor transform, Joint sparse, Remote sensing target, Sparse representation