A Rotation Invariance Spatial Transformation Network for Remote Sensing Image Retrieval

With the development of Geospatial technology and remote sensing technology, a large number of remote sensing images come into application with assist of computers. Although convolutional networks have great performance in computer vision, features extracted by convolutional network doesn't have the characteristic of rotation invariance, which means the current neural network methods can't adapt to rotated objects. Considering the multiangle characteristics of remote sensing images, we proposed a Rotation Invariance Spatial transformation Network (RI-ST-NET) to extract the rotation invariance object features. RI-ST-NET combines convolutional neural networks and the Spatial Transformer Networks (STN) rotating the object to an angle which more easily to identify and is trained by means of Siamese network sharing the same weights of two network branches. Thus RI-ST-NET can adapt to the object features of different rotation patterns which then improved that effectively promote the accuracy of remote sensing retrieval. A Rotation Invariance Spatial Transformation Network combines the advantages of STN and tuple training which can catch the rotation of the same object when used in image retrieval task. A series of evaluation contrast experiments on chosen dataset demonstrate the performance of the proposed method.