Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

Ge, Yun; Jiang, Shunliang; Xu, Qingyong; Jiang, Chang-Long; Ye, Famao

doi:10.1007/s11042-017-5314-5

Cited by 56 publications

(38 citation statements)

References 36 publications

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Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

“…There are mainly two ways to exploit pre-trained CNNs, including regarding fully-connected layers or convolutional layers as the feature representation. Many works [3,22,27,28] have compared the performance of different feature representations extracted among the different networks and different layers. Ge, Jiang, Xu, Jiang and Ye [22] exploit representations from pre-trained CNNs, and feature combination and compression are adopted to improve the feature representation.…”

Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

“…Many works [3,22,27,28] have compared the performance of different feature representations extracted among the different networks and different layers. Ge, Jiang, Xu, Jiang and Ye [22] exploit representations from pre-trained CNNs, and feature combination and compression are adopted to improve the feature representation. The experimental results demonstrate that the pre-trained features and aggregated features are simple, and are able to improve retrieval performance.…”

Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

“…The reason for why CNNs can achieve better performance than handcrafted features is that CNNs can extract high-level semantic features through a large number of convolutional layer stacking with non-linearity. Ge, et al [22] transfers the pre-trained CNNs trained on ImageNet to the remote sensing image data set and compares the features extracted by pre-trained CNNs with handcrafted features for CBRSIR, which leads to the conclusion that the CNNs features outperform the traditional features by a large margin.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval

Xiong

Cui

et al. 2019

Remote Sensing

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Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR). Recently, learning-based features have been widely used in CBRSIR and they show powerful ability of feature representations. In addition, a significant effort has been made to improve learning-based features from the perspective of the network structure. However, these learning-based features are not sufficiently discriminative for CBRSIR. In this paper, we propose two effective schemes for generating discriminative features for CBRSIR. In the first scheme, the attention mechanism and a new attention module are introduced to the Convolutional Neural Networks (CNNs) structure, causing more attention towards salient features, and the suppression of other features. In the second scheme, a multi-task learning network structure is proposed, to force learning-based features to be more discriminative, with inter-class dispersion and intra-class compaction, through penalizing the distances between the feature representations and their corresponding class centers. Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes. Extensive experiments on challenging datasets are conducted to evaluate the effectiveness of our two schemes, and the comparison of the results demonstrate that our proposed schemes, especially the fusion of the two schemes, can improve the baseline methods by a significant margin.

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Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

Section: Learning-based Feature Representation For Cbrsirmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval

Xiong

Cui

et al. 2019

Remote Sensing

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“…The most key and urgent challenge is to extract more compact and discriminative feature representations to efficiently measure the similarity between the query image and retrieval images. There are large amounts of researches focusing on discriminative features extraction which have made tremendous progress by incorporating the effective methods used in the field of general image retrieval [5,9,10]. In the early times, researchers tended to utilize the characteristics like spectral, shape and texture to extract low-level feature representations [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Gou

Xue

et al. 2020

Sensors

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A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.

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