Cross-Source Image Retrieval Based on Ensemble Learning and Knowledge Distillation for Remote Sensing Images

Ma, Jingjing; Shi, Duanpeng; Tang, Xu; Zhang, Xiangrong; Han, Xiao; Jiao, Licheng

doi:10.1109/igarss47720.2021.9553745

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Li et al have proposed a source-invariant deep hashing CNN for RASRSIR between MUL and PAN images, which were optimized in an end-to-end manner using a series of well-designed optimization constraints [123]. To maintain the source discrepancy at the classifier level, Ma et al have presented teacher-ensemble learning with the knowledge distillation method [124]. In [125], a discriminative distillation network was also proposed to ad-dress the inconsistency between different image sources.…”

Section: Rasrsir Methodsmentioning

confidence: 99%

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

Zhou

Guan

et al. 2023

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

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Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest from a large remote sensing (RS) image archive, which has remained to be a hot topic over the past decade. Benefited from the advent and progress of deep learning, RSIR has been promoted by developing novel approaches, constructing new datasets, and exploring potential applications. To the best of our knowledge, there lacks a comprehensive review of RSIR achievements, including systematic and hierarchical categorization of RSIR methods and benchmark datasets in the past decade. This article therefore provides a systematic survey of the recently published RSIR methods and benchmarks by reviewing more than 200 papers. To be specific, in terms of image source, label, and modality, we first group the RSIR methods into some hierarchical categories, each of which is reviewed in detail. Following the categorization of the RSIR methods, we list the benchmark datasets publically available for performance evaluation, and present our newly collected RSIR dataset. Moreover, some of the existing RSIR methods are selected and evaluated on the representative benchmark datasets. The results demonstrate that deep learning-based methods are currently the dominant RSIR approaches and outperform handcrafted feature-based methods by a significant margin. Finally, we discuss the main challenges of RSIR, and point out some potential directions for the future RSIR research.

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Section: Rasrsir Methodsmentioning

confidence: 99%

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

Zhou

Guan

et al. 2023

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

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“…In addition, the FLOPs and parameter volume of ResNet50 are good compared with the other three backbones. In this section, we select twelve methods to verify the proposed DMCL, they are: a deep visual-audio network (DVAN) [48], three models proposed in [65] (one-stream, two-steam, and zero-padding networks), two two-stream networks with and without hierarchical cross-modality metric learning (TONE and TONE + HCML) [66], two dual-path networks introduced in [67] (BCTR and BDTR), a source-invariant deep hashing convolutional neural network (SIDHCNN) [51], a discriminative distillation network (Distillation-ResNet50) [52], an ensemble learning and knowledge distillation network (ELKDN) [68], and a crossmodality shared-specific feature transfer (cm-SSFT) network [18]. Note that the hash code length in SIDHCNN is 32.…”

Section: Reasonableness Of Backbonementioning

confidence: 99%

Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval

Shi

Tang

et al. 2022

Remote Sensing

Self Cite

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Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks.

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“…Fast and accurate retrieval of remote sensing images can help researchers locate relevant information in massive data and provide important support for target recognition. The current research for remote sensing image retrieval are mainly focusing on remote sensing scenes [4,5], and most of them are single modality. It's worth noting that there are few pieces of research on cross-modal ship remote sensing image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Deep Supervised Cross-modal Hashing for Ship Image Retrieval

Guo

Wang

Dan

et al. 2022

J. Phys.: Conf. Ser.

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The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.

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Cross-Source Image Retrieval Based on Ensemble Learning and Knowledge Distillation for Remote Sensing Images

Cited by 7 publications

References 12 publications

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

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

Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval

Deep Supervised Cross-modal Hashing for Ship Image Retrieval

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