Adversarial Hash-Code Learning for Remote Sensing Image Retrieval

Chao Liu, Jingjing Ma, Xu Tang, Xiangrong Zhang, Licheng Jiao

Abstract: Hashing, a useful solution for approximate nearest neighbor (ANN) search, is popular for large-scale image retrieval. In this paper, we presents a deep supervised hashing model for remote sensing image retrieval (RSIR) in the framework of generative adversarial networks (GAN), named GAN-assist Hashing (GAAH). First, to learn the compact and useful hash codes from the images, we define a novel loss function for the generator. The loss function mainly consists of classification, similarity, and bits entropy terms. The classification term makes the hash code is discriminative, the similarity term constrains the binary code is similarity preserving, and the bits entropy term assures the learned code is low-error in the quantization. Second, we construct the unique "true" matrix with the uniform distribution as the input of discriminator to limit the leaned hash codes are bit balanced. The final hash code is learned by a minimax optimization. The positive experimental results on a ground-truth remote sensing image archive validate the usefulness of our GAAH model. Compare with the popular deep hashing methods, our GAAH achieves improved performance.