Hashing-Based Scalable Remote Sensing Image Search and Retrieval in Large Archives

Demir, Begüm; Bruzzone, Lorenzo

doi:10.1109/tgrs.2015.2469138

Cited by 133 publications

(115 citation statements)

References 28 publications

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“…Given two scenes k and l, their corresponding signature vectors are F k and F l , computed using Equation (9). The similarity between the two scenes is defined as: In contrast to the traditional retrieval systems (for which the reference scene can only be of fixed size), we enable the user to select the reference scene more flexibly (see Figure 3b).…”

Section: Similarity Measurementioning

confidence: 99%

“…Li et al [7] proposed a remote sensing image retrieval approach by adopting convolutional neural networks to extract unsupervised features. Demir and Bruzzone [8,9] introduced the hashing methods for large-scale remote sensing (RS) retrieval problems to provide highly time-efficient and accurate search capability within huge data archives. Aptoula [10,11] applied global morphological texture descriptors to the problem of content-based remote sensing image retrieval.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †

Luo

Qin

et al. 2017

Remote Sensing

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Abstract:The management and application of remotely sensed data has become much more difficult due to the dramatically growing volume of remotely sensed imagery. To address this issue, content-based image retrieval (CBIR) has been applied to remote sensing image retrieval for information mining. As a consequence of the growing volume of remotely sensed imagery, the number of different types of image-derived products (such as land use/land cover (LULC) databases) is also increasing rapidly. Nevertheless, only a few studies have addressed the exploration and information mining of these products. In this letter, for the sake of making the most use of the LULC map, we propose an approach for the retrieval of alike scenes from it. Based on the proposed approach, we design a content-based map retrieval (CBMR) system for LULC. The main contributions of our work are listed below. Firstly, the proposed system can allow the user to select a region of interest as the reference scene with variable shape and size. In contrast, in the traditional CBIR/CBMR systems, the region of interest is usually of a fixed size, which is equal to the size of the analysis window for extracting features. In addition, the user can acquire various retrieval results by specifying the corresponding parameters. Finally, by combining the signatures in the base signature library, the user can acquire the retrieval result faster .

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Section: Similarity Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †

Luo

Qin

et al. 2017

Remote Sensing

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“…More specifically, Demir and Bruzzone [32] introduced the kernel-based nonlinear hashing learning methods to the remote sensing community. Afterwards, Li and Ren [33] proposed a novel unsupervised hashing method called partial randomness hashing (PRH), which aims to enable an efficient hashing function construction and learns a transformation weight matrix based on the training remote sensing images in an efficient way.…”

Section: Introductionmentioning

confidence: 99%

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Tao

et al. 2017

IJGI

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Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing LSRSIR methods take only one type of feature descriptor as the input of hashing learning methods and ignore the complementary effects of multiple features, which may represent remote sensing images from different aspects. Different from the existing LSRSIR methods, this paper proposes a flexible multiple-feature hashing learning framework for LSRSIR, which takes multiple complementary features as the input and learns the hybrid feature mapping function, which projects multiple features of the remote sensing image to the low-dimensional binary (i.e., compact) feature representation. Furthermore, the compact feature representations can be directly utilized in LSRSIR with the aid of the hamming distance metric. In order to show the superiority of the proposed multiple feature hashing learning method, we compare the proposed approach with the existing methods on two publicly available large-scale remote sensing image datasets. Extensive experiments demonstrate that the proposed approach can significantly outperform the state-of-the-art approaches.

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“…In recent decades, different types of CB-HRRS-IR have been proposed. Generally, existing CB-HRRS-IR methods can be classified into two categories: those that take only one single image as the query image [1,2,[4][5][6][7] and those that simultaneously take multiple images as the query images [3,8].…”

Section: Introductionmentioning

confidence: 99%

“…For charactering high-resolution remote sensing images, low-level features such as spectral features [9,10], shape features [11,12], morphological features [5], texture features [13], and local invariant features [2] have been adopted and evaluated in the CB-HRRS-IR task. Although low-level features have been employed with a certain degree of success, they have a very limited capability in representing the high-level concepts presented by remote sensing images (i.e., the semantic content).…”

Section: Introductionmentioning

confidence: 99%

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Zhang

Tao

et al. 2016

Remote Sensing

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Abstract:With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.Keywords: high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion

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Hashing-Based Scalable Remote Sensing Image Search and Retrieval in Large Archives

Cited by 133 publications

References 28 publications

Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †

Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

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