Multiview Inherent Graph Hashing for Large-Scale Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is one of the most challenging tasks in remote sensing (RS) community. With the volume of RS images increases explosively, conventional exhaustive search is often infeasible in real applications. Recently, hashing has attracted increasing attention for RSIR due to significant advantage in terms of computation and storage. Hashing first generates a set of short compact hash codes to encode RS images, and then applies hash codes for effective RSIR. Multiview hashing usually achieves promising RSIR performance by fusing multiples kinds of RS image features. Conventional multiview hashing simply predefines graph Laplacian in each view, which cannot effectively explore underlying similarity structures among RS images. To address this issue, this article proposes a novel multiview inherent graph hashing (MvIGH) for RSIR. MvIGH captures the latent similarities among RS images, and adaptively learns weights of each view to characterize its contribution. In addition, MvIGH further minimizes the quantization errors. We develop an efficient alternating algorithm to solve the formulated optimization problem. The experiments on three public RS image datasets demonstrate the superiority of the proposed method over the existing multiview hashing methods in RSIR tasks.