Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data

Wang, Xianghai; Feng, Yining; Song, Ruoxi; Mu, Zhenhua; Song, Chuanming

doi:10.1016/j.inffus.2021.12.008

Cited by 62 publications

(19 citation statements)

References 35 publications

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“…Big data teaching refers to the use of big data in teaching by schools and teachers to build an informationized and personalized teaching environment and provide teachers and students with a resource pool to achieve common progress between teachers and students [16][17][18][19][20]. In big data teaching, teachers can use relevant software to build the teaching environment and make full use of the big data function.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Pan

2022

Discrete Dynamics in Nature and Society

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With the rapid development of educational information technology, “online and offline” hybrid teaching has become the main trend of foreign language teaching reform in colleges and universities. The blended teaching mode integrates the traditional teaching mode with modern educational technology and reconstructs the traditional college English teaching mode in the aspects of teaching object, teaching content, teaching method, teaching environment, and teaching evaluation. Based on the multisource data such as students’ learning situation, learning duration, and academic performance, this paper builds a student English learning analysis system that realizes the integration of multisource data, completes the unified processing and analysis of multisource data, and then displays it through the interface. The system helps English teachers to guide students in different degrees according to the students’ English learning situation and academic performance, so that English teachers have a clearer and more comprehensive understanding of students’ learning and living conditions and timely guide students with incorrect learning attitudes, so as to avoid students’ detours.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Pan

2022

Discrete Dynamics in Nature and Society

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“…The data-level fusion strategy usually fuses raw or pre-processed data from multi-resolution images [9] or multi-spectral images [10], and so forth, usually appearing in early works. Feature-level fusion [11][12][13][14][15][16][17][18][19][20][21][22], on the other hand, combines multiple intermediate features extracted from multi-view data. The fused features were then used in downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The fused features were then used in downstream tasks. After the emergence of DNN, feature-level fusion often adopts the network structure of multi-input-single-output [14,[17][18][19]21,22], in which some layers concatenate the intermediate features of multi-views into subsequent layers. Decision-level fusion adopts different fusion rules to aggregate predictions from multiple classifiers, each of which is obtained from a separate model.…”

Section: Introductionmentioning

confidence: 99%

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-View Images

et al. 2023

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Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN)-based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexibly in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness.

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“…Compared with HSI, light detection and ranging (LiDAR) uses pulsed laser to measure the range, which is an active remote sensing method [6,7]. Moreover, LiDAR is not easily affected by weather conditions, which can not only provide the height and shape information of the scene, but also has intense accuracy and flexibility [8,9]. HSIs can provide various spectral information, and LiDAR data has accurate spatial and elevation information.…”

Section: Introductionmentioning

confidence: 99%

Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network

2022

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Remote sensing image classification is a prominent topic in earth observation research, but there is a performance bottleneck when classifying single-source objects. As the types of remote sensing data are gradually diversified, the joint classification of multi-source remote sensing data becomes possible. However, the existing classification methods have limitations in heterogeneous feature representation of multimodal remote sensing data, which restrict the collaborative classification performance. To resolve this issue, a position-channel collaborative attention network is proposed for the joint classification of hyperspectral and LiDAR data. Firstly, in order to extract the spatial, spectral, and elevation features of land cover objects, a multiscale network and a single-branch backbone network are designed. Then, the proposed position-channel collaborative attention module adaptively enhances the features extracted from the multi-scale network in different degrees through the self-attention module, and exploits the features extracted from the multiscale network and single-branch network through the cross-attention module, so as to capture the comprehensive features of HSI and LiDAR data, narrow the semantic differences of heterogeneous features, and realize complementary advantages. The depth intersection mode further improves the performance of collaborative classification. Finally, a series of comparative experiments were carried out in the 2012 Houston dataset and Trento dataset, and the effectiveness of the model was proved by qualitative and quantitative comparison.

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Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data

Cited by 62 publications

References 35 publications

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-View Images

Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network

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