Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

Abstract

As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.

Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions

“…Lukac et al have improved the well-known KLSH (kernelized locality sensitive hashing) method using Graphical Processing Units (GPU) to make it feasible for parallelization, and thus performing fast parallel image retrieval [94]. Kong et al have proposed a low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR [95]. To improve the performance of unsupervised hashing methods, self-supervised methods, semi-supervised methods, and methods relying on pseudo label have been explored.…”

Attention-based localized hash retrieval for large-scale remote sensing images using deep feature splitting strategy

“…A hash-based retrieval of RSI using DenseNet with a convolutional block-attention module is explained 26 . A low-rank hypergraph hashing is developed 27 . Several challenges involved in hashing-based retrieval are described 28 .…”

“…26 A low-rank hypergraph hashing is developed. 27 Several challenges involved in hashing-based retrieval are described. 28 Triplet-based metric learning for deep hashing has been developed, 19 and is quite promising in the RS domain.…”

Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”

“…In this Special Issue, several stages of sub-pixel image processing are approached incorporating advanced techniques such as neural networks, deep learning, and probabilistic non-Gaussian mixture models. This Special Issue consists of nine research papers [1][2][3][4][5][6][7][8][9]. All the methods proposed in the papers were validated using real hyperspectral data and benchmarked with state-of-the-art methods, thus comprehensively demonstrating the theoretical and practical contributions of the papers.…”

“…Retrieving similar remote sensing images from large-scale datasets is very important and demanding. To reduce the retrieval complexity of remote sensing image retrieval, a new hash learning method is proposed in [2]. This method generates compact hash codes using low-rankness constraints on the projection matrix to exploit its global structure and hypergraphs to capture the high-order relationship among the data.…”