Aggregated Deep Local Features for Remote Sensing Image Retrieval

Imbriaco, Raffaele; Sebastian, Clint; Bondarev, Egor; With, Peter de

doi:10.3390/rs11050493

Cited by 63 publications

(44 citation statements)

References 59 publications

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“…(4) To verify the superiority of our proposed optimal structured loss, we conduct the experiment on multiple remote sensing datasets. The retrieval performance is boosted with approximately 5% on these public remote sensing datasets compared with the existing methods [28,[49][50][51] and this demonstrates that our proposed method achieves the state-of-the-art results in the task of RSIR.…”

Section: Introductionmentioning

confidence: 74%

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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Section: Introductionmentioning

confidence: 74%

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Gou

Xue

et al. 2020

Sensors

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“…The basic idea of ANN is to replace the exact match with the approximate optimal, and this greatly improves the retrieval efficiency while ensuring the accuracy of image retrieval. Among them, the hash learning method [32] is a commonly used method of ANN. The hash learning method is widely used in large-scale image retrieval due to its advantages in speed and storage.…”

Section: Remote Sensing Image Retrieval Methodsmentioning

confidence: 99%

“…The hash learning method is widely used in large-scale image retrieval due to its advantages in speed and storage. For example, the non-linear hashing method based on RS two kernels, achieves real-time search and fast detection through mapping image feature vectors of high dimensional image into compact truncated hash codes [32]. A hashing-based approach introduces a hashing algorithm to encode RS images [31].…”

Section: Remote Sensing Image Retrieval Methodsmentioning

confidence: 99%

“…In recent years, deep learning methods have also been applied to hash coding to obtain better coding effects [33]. The partial random hash of the random projection is generated in an unsupervised manner, the image is mapped to the Hamming space to obtain the low-latitude expression of the image, and then the model is trained [32]. Unsupervised strategies [15], metric-based learning of the Hash network [34], and deep hash neural networks [33] are also methods for resolving large-scale remote sensing images.…”

Section: Remote Sensing Image Retrieval Methodsmentioning

confidence: 99%

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Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

Fan

Zhao

2020

Remote Sensing

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Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

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“…Following [14], similar images and sounds can be considered as the ground-truth neighbors. These evaluating metrics-mean average precision (mAP) and the precision in top m of the ranking list (precision@m)-were exploited for assessing the experimental results [49][50][51][52]. Precision represents the proportion of the correct number of samples to the total number of samples in the ranking list [53].…”

Section: Dataset and Evaluation Protocolsmentioning

confidence: 99%

A Deep Hashing Technique for Remote Sensing Image-Sound Retrieval

Chen

2019

Remote Sensing

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With the rapid progress of remote sensing (RS) observation technologies, cross-modal RS image-sound retrieval has attracted some attention in recent years. However, these methods perform cross-modal image-sound retrieval by leveraging high-dimensional real-valued features, which can require more storage than low-dimensional binary features (i.e., hash codes). Moreover, these methods cannot directly encode relative semantic similarity relationships. To tackle these issues, we propose a new, deep, cross-modal RS image-sound hashing approach, called deep triplet-based hashing (DTBH), to integrate hash code learning and relative semantic similarity relationship learning into an end-to-end network. Specially, the proposed DTBH method designs a triplet selection strategy to select effective triplets. Moreover, in order to encode relative semantic similarity relationships, we propose the objective function, which makes sure that that the anchor images are more similar to the positive sounds than the negative sounds. In addition, a triplet regularized loss term leverages approximate l1-norm of hash-like codes and hash codes and can effectively reduce the information loss between hash-like codes and hash codes. Extensive experimental results showed that the DTBH method could achieve a superior performance to other state-of-the-art cross-modal image-sound retrieval methods. For a sound query RS image task, the proposed approach achieved a mean average precision (mAP) of up to 60.13% on the UCM dataset, 87.49% on the Sydney dataset, and 22.72% on the RSICD dataset. For RS image query sound task, the proposed approach achieved a mAP of 64.27% on the UCM dataset, 92.45% on the Sydney dataset, and 23.46% on the RSICD dataset. Future work will focus on how to consider the balance property of hash codes to improve image-sound retrieval performance.

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Aggregated Deep Local Features for Remote Sensing Image Retrieval

Cited by 63 publications

References 59 publications

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

A Deep Hashing Technique for Remote Sensing Image-Sound Retrieval

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