Primitive cluster sensitive hashing for scalable content-based image retrieval in remote sensing archives

Reato, Thomas; Demir, Begüm; Bruzzone, Lorenzo

doi:10.1109/igarss.2017.8127424

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Ye et al [34] proposed a hashing retrieval framework for remote sensing images, which maps multiple feature descriptors into compact binary hash codes. Reato et al [35] proposed a primitive cluster sensitive hashing for unsupervised remote sensing image retrieval, which employs multiple hash codes in hash function construction and matching phase.…”

Section: ) Hashing Methods Without Deep Learningmentioning

confidence: 99%

Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

Zhang

Zheng

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the joint advancement of numerous related fields of remote sensing, the amount of remote sensing data is growing exponentially. As an essential remote sensing Big Data management technique, content-based remote sensing image retrieval has attracted more and more attention. A novel deep attention hashing with distance-adaptive ranking (DAH) is proposed for remote sensing image retrieval in this article. First, a channel-spatial joint attention mechanism is employed for feature extraction of remote sensing images to make the proposed DAH method focus more on the critical details of the remote sensing images and suppress irrelevant regional responses. Second, a novel balanced pairwise weighted loss function is proposed to enable discrete hash codes to participate in neural network training, which contains pairwise weighted similarity loss, classification loss, and quantization loss. The pairwise weighted similarity loss is designed to decrease the impact of the imbalance of positive and negative sample pairs. The classification loss and quantization loss are added to the loss function to decrease background interference and information loss during the quantization phase, respectively. Finally, a distanceadaptive ranking strategy with category-weighted Hamming distance is presented in the retrieval phase to utilize the category probability information fully. Experiments on benchmark datasets compared with state-of-the-art methods demonstrate the effectiveness of the proposed DAH method.

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Section: ) Hashing Methods Without Deep Learningmentioning

confidence: 99%

Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

Zhang

Zheng

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To address these issues and accurately characterize the primitives present in RS images using hashing, in this letter, we introduce a novel unsupervised strategy, which represents each image with multi-hash codes, where each code corresponds to a primitive. The contribution of this letter, which significantly extends the work presented in [8], consists in introducing a novel strategy to describe each image by a set of descriptors of primitive-sensitive clusters and their hash codes for large-scale RS retrieval problems. The experimental results obtained on a benchmark archive demonstrate that the proposed method outperforms the stateof-the-art single-code unsupervised hashing method.…”

Section: Introductionmentioning

confidence: 96%

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

Reato

Demir

Bruzzone

2019

IEEE Geosci. Remote Sensing Lett.

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Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally represent the complex information content of RS images. To overcome this problem, in this letter, we present a simple yet effective unsupervised method that represents each image with primitive-cluster sensitive multi-hash codes (each of which corresponds to a primitive present in the image). To this end, the proposed method consists of two main steps: 1) characterization of images by descriptors of primitive-sensitive clusters and 2) definition of multi-hash codes from the descriptors of the primitive-sensitive clusters. After obtaining multi-hash codes for each image, retrieval of images is achieved based on a multi-hash-code-matching scheme. Any hashing method that provides single-hash code can be embedded within the proposed method to provide primitive-sensitive multihash codes. Compared with state-of-the-art single-code hashing methods in RS, the proposed method achieves higher retrieval accuracy under the same retrieval time, and thus it is more efficient for operational applications.

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

Kang

Liu

Sun

2018

2018 24th International Conference on Pattern Recognition (ICPR)

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Primitive cluster sensitive hashing for scalable content-based image retrieval in remote sensing archives

Cited by 5 publications

References 5 publications

Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

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

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