Object Classification of Remote Sensing Images Based on Partial Randomness Supervised Discrete Hashing

Recently, object classification of remote sensing images has attracted more and more research interests due to the development of satellite and aerial vehicle technologies. Hashing learning is an efficient method to handle the huge amount of the remote sensing data. In this paper, we proposed a novel hashing learning method named partial randomness supervised discrete hashing (PRSDH), which combines data-dependent methods and data-independent methods. It jointly learns a discrete binary codes generation and partial random constraint optimization model. By random projection, the computation complexity is reduced effectively. With the weight matrix derived from the training data, the semantic similarity between the data can be well preserved while generating the hashing codes. For the discrete constraint problem, this paper adopts the discrete cyclic coordinate descent (DCC) algorithm to optimize the codes bit by bit. The experimental results show that PRSDH outperforms other comparative methods and demonstrated that PRSDH has good adaptability to the characteristic of remote sensing object.