Mohamed Farah

Remote Sensing Image Retrieval (RSIR) is quite a challenging topic due to the complexity of High-Resolution Remote Sensing (HRRS) imagery. Recently, most researches are focusing on feature extraction using learned features instead of traditional hand-crafted features for RSIR. However, on the one hand, learning discriminative feature representations is not sufficient due to the limited ability of the standard metric distances for matching similar images. On the other hand, the high dimensionality of the learned features makes the retrieval process not efficient for large-scale collections of HRRS images. To address these issues, we propose a Triplet Low-Dimensional Convolutional Neural Network (TLDCNN) which is based on the LDCNN model combined with triplet loss function. We use end-to-end training process to automatically learn fine-grained image similarity directly from input triplet images. The experiment results demonstrated the effectiveness of the proposed network on three publicly available HRRS image datasets for HRRSIR.