Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class.
Citation: Song Yang, Huibin Wang, Hongmin Gao, Lili Zhang. Few-shot remote sensing scene classification based on multi subband deep feature fusion[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12889-12907. doi: 10.3934/mbe.2023575
[1] | Ziyue Wang, Junjun Guo . Self-adaptive attention fusion for multimodal aspect-based sentiment analysis. Mathematical Biosciences and Engineering, 2024, 21(1): 1305-1320. doi: 10.3934/mbe.2024056 |
[2] | Shuai Cao, Biao Song . Visual attentional-driven deep learning method for flower recognition. Mathematical Biosciences and Engineering, 2021, 18(3): 1981-1991. doi: 10.3934/mbe.2021103 |
[3] | Quan Zhu, Xiaoyin Wang, Xuan Liu, Wanru Du, Xingxing Ding . Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus. Mathematical Biosciences and Engineering, 2023, 20(10): 18566-18591. doi: 10.3934/mbe.2023824 |
[4] | Xing Hu, Minghui Yao, Dawei Zhang . Road crack segmentation using an attention residual U-Net with generative adversarial learning. Mathematical Biosciences and Engineering, 2021, 18(6): 9669-9684. doi: 10.3934/mbe.2021473 |
[5] | Zhijing Xu, Yang Gao . Research on cross-modal emotion recognition based on multi-layer semantic fusion. Mathematical Biosciences and Engineering, 2024, 21(2): 2488-2514. doi: 10.3934/mbe.2024110 |
[6] | Wenbo Yang, Wei Liu, Qun Gao . Prediction of dissolved oxygen concentration in aquaculture based on attention mechanism and combined neural network. Mathematical Biosciences and Engineering, 2023, 20(1): 998-1017. doi: 10.3934/mbe.2023046 |
[7] | Xiaotong Ji, Dan Liu, Ping Xiong . Multi-model fusion short-term power load forecasting based on improved WOA optimization. Mathematical Biosciences and Engineering, 2022, 19(12): 13399-13420. doi: 10.3934/mbe.2022627 |
[8] | Suqi Zhang, Xinxin Wang, Wenfeng Wang, Ningjing Zhang, Yunhao Fang, Jianxin Li . Recommendation model based on intention decomposition and heterogeneous information fusion. Mathematical Biosciences and Engineering, 2023, 20(9): 16401-16420. doi: 10.3934/mbe.2023732 |
[9] | Feng Li, Mingfeng Jiang, Hongzeng Xu, Yi Chen, Feng Chen, Wei Nie, Li Wang . Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction. Mathematical Biosciences and Engineering, 2024, 21(3): 4085-4103. doi: 10.3934/mbe.2024180 |
[10] | Tianshu Wei, Jinjie Huang, Cong Jin . Zero-shot learning via visual-semantic aligned autoencoder. Mathematical Biosciences and Engineering, 2023, 20(8): 14081-14095. doi: 10.3934/mbe.2023629 |
Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class.
It is widely recognized that adenosine triphosphate (ATP) acts as an extracellular messenger by activating purinergic receptors via autocrine and paracrine signaling [1]. In addition to its physiological role, an increase in extracellular ATP concentrations has been implicated as a “danger signal” or “danger-associate molecular pattern (DAMP)” in the pathophysiology of pulmonary diseases such as cystic fibrosis, asthma, chronic obstructive pulmonary disease (COPD) and pulmonary fibrosis [2,3,4,5,6]. Therefore, the concentration of extracellular ATP in vivo is strictly controlled at a baseline steady state by balancing the amounts of ATP release and hydrolysis of ATP by ecto-ATPases [7,8]. In the respiratory system, ATP is released from various kinds of cells including neurons, immune cells, cancer cells, airway epithelial cells, alveolar epithelial cells, airway smooth muscle (ASM) cells, lung fibroblasts and pulmonary artery endothelial cells in response to chemical and mechanical stimuli [9,10,11,12,13,14,15]. In this review, we focus on cellular ATP release, specifically methods for visualization and mechanisms, in the lung and airway resident cells.
ATP plays a pivotal role in homeostasis of the airway by regulating epithelial mucociliary clearance and host defenses against bacterial infection [1]. The effects of extracellular purine nucleotides are mediated by their specific P2 receptors, subclassified as P2X and P2Y receptors [1]. P2X receptors are members of Ca2+-permeable ion channels, while P2Y receptors are coupled to G proteins [16]. Extracellular ATP causes anion secretion from airway epithelial cells [17,18] and stimulates surfactant secretion from type II alveolar epithelial cells [19]. ATP and its metabolite adenosine are important luminal autocrine and paracrine signals that regulate the hydration of airway epithelial cells [20,21]. In the lung, ATP released from alveolar epithelial cells stimulates endothelial nitric oxide (NO) production by pulmonary microvascular endothelial cells [22], indicating that ATP is involved in regulation of mechanisms of gas exchange by affecting microvascular circulation.
Cystic fibrosis lung disease, an extremely rare disease in Japanese, is a hereditary disease caused by a mutation in the cystic fibrosis transmembrane regulator (CFTR) gene [23]. Because CFTR is essential for maintaining water balance via the transport of Cl- ions across cell membranes of airway epithelial cells, its dysfunction results in the production of thick mucus and airway dehydration in patients with cystic fibrosis [1,23]. The levels of lipoxin A4, which is synthesized from arachidonic acid by the action of 5-lipoxygenase, have been reported to be decreased in cystic fibrosis [24]. Application of lipoxin A4 to airway epithelial cell lines derived from cystic fibrosis induces ATP release and subsequent activation of P2Y11 receptor, which leads to an increase in height of airway surface liquid layer [25]. Therefore, lipoxin A4 and ATP/purinergic signaling is expected as potentially a novel therapy for patients with cystic fibrosis lung disease [1].
In addition to its physiological roles, extracellular ATP is recognized as one of the DAMPs, which contribute to apoptosis and inflammation in the lung and airway [26]. Therefore, extracellular ATP and purinergic signaling are considered to be involved in various respiratory diseases including asthma, COPD, pulmonary fibrosis, lung injury and lung cancer [1,3,4,9,15,27,28]. Lung hypoxia causes ATP release, which leads to proliferation of adventitial fibroblasts of the pulmonary artery and induces transformation to myofibroblasts via autocrine/paracrine mechanisms [29], indicating the role of ATP in vascular remodeling inpulmonary arterial hypertension. A blockade of purine receptors may represent a potential therapy for abnormal inflammatory responses to extracellular ATP [30]. Here, we briefly summarize examples of the roles of extracellular ATP in the pathogenesis of asthma, COPD, pulmonary fibrosis and lung injury.
An association between ATP and the pathogenesis of asthma and COPD has been proposed [1,2,4,9,31,32]. Contraction of ASM, which is regulated by intracellular Ca2+ concentrations ([Ca2+]i) and sensitivity to intracellular Ca2+, plays a central role in the airway narrowing of asthma and is partially involved in the pathophysiology of COPD [33]. Activation of P2X and P2Y receptors by ATP causes contraction of ASM tissues [34,35]. Extracellular ATP directly mobilizes [Ca2+]i in ASM cells [34,36,37]. In tracheal smooth muscle tissues isolated from guinea pigs, ATP enhances methacholine-induced contraction [38]. This agonist-synergism of purinergic and muscarinic receptors mediated by the Ca2+ sensitivity of contractile proteins [38] may lead to airway hyperresponsiveness in patients with asthma. The binding of extracellular ATP to P2X and P2Y receptors induces the recruitment and activation of inflammatory cells such as neutrophils, macrophages and dendritic cells [39]. Mortaz et al. demonstrated that exposure to cigarette smoke leads to the release of ATP into the airways, which may induce release of IL-8 and elastase by neutrophils [28], both of which are directly related to development of pulmonary emphysema and inflammatory response in the pathophysiology of COPD. There is evidence that ATP accumulates in the airways of patients with asthma and COPD [3,28]. Thus, it is suggested that ATP contributes to contraction of ASM and inflammation in the pathophysiology of asthma and COPD.
A major cause of progressive pulmonary fibrosis is dysregulated wound healing after lung inflammation or damage in patients with idiopathic pulmonary fibrosis (IPF) or severe lung injury such as acute respiratory distress syndrome (ARDS). ATP concentrations in bronchoalveolar lavage fluid are increased in patients with IPF and murine models of experimental lung fibrosis [5]. Extracellular ATP and purinergic signaling plays an important role in the pathogenesis of pulmonary fibrosis [40]. Previous studies have shown that ATP regulates cellular properties and differentiation of lung fibroblasts, a key player in lung fibrosis. ATP elevates [Ca2+]i and induces expression of P2Y6 and P2Y11 receptor genes in human lung fibroblasts [41].
Mechanical cues including shear stress, matrix stiffness and stretching are involved in the mechanisms underlying the pathogenesis of pulmonary fibrosis and lung injury [42,43,44,45]. These mechanical stimuli regulate cellular properties such as proliferation, differentiation, migration, cytokine production and ion channel gating via activating mechanotransduction in the lung and airway resident cells [46,47,48,49,50,51,52]. In mechanically ventilated patients with respiratory failure due to acute lung injury, ARDS and acute exacerbation of IPF, the lung is exposed to excessive stretch, which often causes further damage and fibrosis, called ventilator-induced lung injury (VILI) [53,54,55]. In rat models of VILI, ATP levels of bronchoalveolar lavage fluid were significantly increased by high pressure mechanical ventilation [56]. Intratracheal ATP administration also directly increased pulmonary edema [56]. These findings further suggest that ATP acts as an inflammatory mediator and regulates permeability of pulmonary microvasculature in pathophysiology of VILI.
ATP is released from cells upon cellular injury as a “find-me signal” as well as from pathogens in diseased conditions [57]. Moreover, ATP is released from normal cells as a physiological phenomenon [8,58,59]. For example, ATP released from vascular endothelial cells in response to shear stress by fluid flow tightly regulates pulmonary circulation [60]. Endothelial cells of mice lacking P2X4 channels fail to induce [Ca2+]i elevation and NO production in response to blood flow, which results in impaired flow-dependent control of vascular tone and remodeling [14,15,61]. In addition to physiological phenomena, cellular ATP release is enhanced by stress conditions such as inflammatory processes, hypoxia and mechanical stresses without cell damage. Mechanical stresses such as stretch and hypotonic cell swelling induce ATP release from the cells including A549 lung adenocarcinoma cell lines, Calu-3 cells, airway epithelial cells, ASM cells and lung fibroblasts [12,13,40,62,63,64,65]. In several kinds of cell types, mechanically-induced ATP regulates Ca2+ signaling, a universal second messenger for most important cellular functions, via an autocrine/paracrine mechanism. Shear stress evokes ATP release, which induces subsequent intracellular Ca2+ waves in pulmonary artery endothelial cells [14]. In HaCaT keratinocytes, ATP released in response to mechanical stretch leads to a subsequent [Ca2+]i elevation by activating P2Y receptors [49]. Inconsistent with these findings, the [Ca2+]i elevation caused by mechanical stretch was not inhibited by ATP diphosphohydrolase apyrase or the purinergic receptor antagonist suramin in primary human lung fibroblasts [12]. Thus, the contribution of autocrine/paracrine mechanisms by released ATP on regulation of [Ca2+]i differs among cell types.
The standard technique to assess extracellular ATP in bulk solutions is observing the bioluminescence of the ATP-dependent luciferase-mediated oxidation of luciferin. This luciferin-luciferase (L-L) biuoluminescence assay is a useful tool to quantify ATP concentrations in bulk media both in vitro and in vivo. An example for measurements of ATP released from human pulmonary microvascular endothelial cells is shown in Figure 1. Using a cell stretch apparatus, uniaxial cyclic stretch (30 cycle/min for 15 min) was applied to the silicone chamber on which the cells were grown. The bulk concentrations of ATP in the cell culture supernatant were significantly elevated by 20% strain but not by 4% strain (Figure 1).
Several laboratories have attempted to visualize the ATP release from individual cells in real time using biosensor techniques [14,58,66,67,68,69]. As for fluorescent methods, Corriden et al. utilized a tandem-enzyme system that generates fluorescent NADPH when ATP is present and visualizes released ATP from individual Jurkat T cells and human neutrophils [70]. Recently, using novel small molecular fluorescent ATP probes 1-2Zn (II) (water soluble) and 2-2Zn (II) (membrane bounded), developed by Ojida et al. [71,72], Ledderose et al. demonstrated that immune stimulation of neutrophils and T cells caused an instantaneous ATP release, followed by a second phase of ATP release that was localized to the immune synapse of T cells or the leading edge of polarized neutrophils [73]. Combining these ATP imaging with mitochondrial probes provided evidence for a close spatial relationship between mitochondrial activation and localized ATP release in these immune cells [73].
As for bioluminescent methods using L-L, Nakamura et al. developed a novel chemiluminescence imaging method by utilizing a biotin-luciferase chimera protein that can be stably immobilized on a biotinylated cell surface with streptavidin [67]. Using this probe and high-sensitive EM-CCD camera, Yamamoto et al. demonstrated that the localized ATP release in response to fluid shear stress occurred at caveolae in pulmonary artery endothelial cells [14]. Furuya et al. improved the luminescence imaging using an image intensifier with EM-CCD camera under a upright microscope and enabled simultaneous observation of bioluminescence of ATP and differential interference contrast (DIC) images of the cells and tissues using infrared (IR) optics [13,49,65,69]. Using this imaging system coupled with a cell-stretching apparatus, we visualized the extracellular ATP released from human ASM cells [13] and pulmonary microvascular endothelial cells in response to a single mechanical stretch in real time. Figure 2A shows representative luminescent images of released ATP (red) and IR-DIC images for pulmonary microvascular endothelial cells (green) during stretching (23% strain, 1 s) (see also Supplemental movie E1). Although ATP responses to stretch were increase in number of the responded cells with stretching strength, the spatial distribution of releasing cells were restricted and heterogeneous even in cultured homogeneous cells, consistent with findings in mammary epithelial cells, ASM cells and A549 cells [13,65,69]. Figure 2B and 2C show the pseudo color images of the luminescence and the time course of the luminescence changes in 30 responded cells, respectively. The intensity and duration of ATP release from individual cells were rather variable. The duration of ATP release was in the range from about 10 to 80 s and seemed to be separated into two groups, fast and slow. Taken together, the real-time ATP imaging has an advantage in assessing cell populations with heterogeneous responsibility as well as in analyzing kinetic aspects of released ATP at the single cell and tissue levels [13,69].
Recently, Furuya et al. have successfully visualized inflation induced ATP release in the rat lung tissue ex vivo [74]. With L-L solution introduced into airspaces brief inflation of the lung (1 s, ~20 cm H2O) induced transient ATP release in a limited number of air-inflated alveolar sacs. Released ATP remained spatially restricted to single alveolar sacs or their clusters. With L-L introduced into blood vessels, inflation induced transient ATP release, which occurred in small patch-like areas, the size of alveolar sacs and spread to surrounding capillaries. Findings suggest that physiological and pathophysiological inflation of the lung induces ATP release in both alveoli sacs and the surrounding blood capillary network and the functional units of ATP release presumably consist of alveolar sacs or their clusters [74]. These imaging approaches will enable future investigations of ATP release in tissues, organs and in vivo, giving new insights into the working mechanisms and complexity of the purinergic signaling in the respiratory system.
Multiple mechanisms and pathways by which cells release ATP during mechanical deformation and chemical stimulation have been proposed [59,75]. It has been suggested that various kinds of membrane channels such as connexin and pannexin hemichannels, maxi-anion channels, volume-regulated anion channels, CFTR and P2X7 receptor mediate ATP release [8,49,58,76,77]. In addition to a conductive pathway via channels, exocytosis of ATP-enriched vesicles is involved in the mechanisms of ATP release [8,78]. Here, we summarize the role of vesicular exocytosis in ATP release.
Staining of cellular ATP stores with quinacrine or the fluorescent ATP analogue reveals a distribution of fluorescence consistent with ATP-enriched vesicles [68,78,79]. Thus, an active transport system for ATP uptake into vesicles is present. Sawada et al. identified SLC17A9 protein as the vesicular nucleotide transporter (VNUT) that plays a central role in the mechanisms of ATP uptake into intracellular vesicles [80]. There is strong evidence that the vesicular exocytosis is the major pathway for cellular ATP release in the lung and airway cells [13,64,65]. Exocytosis of vesicles is regulated by intracellular free Ca2+ [81,82]. Okada et al. found that inhibitors of Ca2+-regulated vesicular exocytosis such as 1, 2-bis(o-aminophenoxy)ethane-N, N, N’, N’-tetraacetic acid tetraacetoxymethyl ester (BAPTA-AM), an intracellular Ca2+ chelator, N-ethylmaleimide (NEM), an inhibitor of the fusion of vesicles to the plasma membrane and bafilomycin A1, which depletes ATP within vesicular pools by inhibiting vesicular H+-ATPase, significantly inhibited the ATP release induced by hypotonic stress in human bronchial epithelial cells [64]. Similar results were observed in primary human ASM cells. BAPTA-AM, NEM, bafilomycin A1 and monensin, an inhibitor of vesicular transport, significantly inhibited the increase of ATP concentrations in the cell supernatant induced by cyclic stretch in human ASM cells [13]. The results in the previous studies further suggest that transport of ATP into vesicular pools plays a role in the ATP release from human ASM cells, A549 adenocarcinoma cell lines and normal airway epithelial cells [13,64]. Sesma et al. found that VNUT regulates the ATP concentration in mucin granules as well as the amount of released ATP in human airway epithelial Calu-3 cells [83]. Expression of VNUT mRNA and ATP release was enhanced by inflammation in primary human bronchial epithelial cells obtained from patients with cystic fibrosis and normal subjects [64]. Therefore, VNUT plays a key role in ATP uptake and subsequent ATP release via vesicular exocytosis in the lung and airway cells.
It is likely that the relative contribution of vesicular exocytosis and conductive pathways to the amount of ATP released depends on cell type. The stretch-induced ATP release from human ASM cells was not significantly affected by carbenoxolone, an inhibitor of pannexins and connexins [13]. Similar findings were observed in A549 cells [65]. In contrast, pannexin 1 contributes to ATP release onto the apical airway surface from normal human bronchial epithelial cells [62]. However, the mechanisms of cellular ATP release have not been fully elucidated.
Extracellular vesicles, exosomes and membrane-derived microvesicles, are released from the cell. Because extracellular vesicles contain cytoplasmic proteins, cytokines, mRNA and microRNA, they act as important mediators of cell-to-cell communication and the intercellular microenvironment [84]. Moreover, extracellular vesicles are considered to play a role in maintenance of normal lung physiology as well as pathogenesis of respiratory diseases such as COPD and lung cancer[85]. Unlike cytoplasmic vesicles, it is not known whether the released extracellular vesicles contain ATP as a tool for cell-to-cell communication. In macrophages, monocytes, dendritic cells and microglial cells, which express P2X7 receptors, extracellular ATP induces maturation and subsequent release of IL-1β via activating P2X7 receptors [86]. Interestingly, exosome and membrane-derived microvesicles, which contain IL-1β, have been proposed as one of pathways for the ATP-induced IL-1β release [86]. Because an increase in [Ca2+]i has been proposed as a mechanism of formation of membrane-derived microvesicles [87], extracellular ATP or released ATP may regulate the formation of microvesicles via purinergic receptors in the lung and airway cells. Future studies are required to elucidate this possibility.
In this review, we focused on the pathophysiology and possible mechanisms of ATP release from the airway and lung cells. However, the precise mechanisms of ATP release from the cell have not been fully elucidated. For example, we know little about how ATP release is related to the dynamics of mitochondria in which ATP is synthetized.Understanding the mechanisms of ATP dynamics will bring important insights into the pathophysiology of respiratory diseases and normal respiratory physiology.
This work was supported by JSPS KAKENHI Grant Numbers 25461188 and 16K09578 to S.I. and 24590274 and 15K09174 to K.F. The authors thank Ms. Katherine Ono for providing language help. We thank Dr. Norihiro Takahara for his experimental work.
The authors declare that there is no conflict of interests.
[1] | Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in Proceedings of Sigspatial International Conference on Advances in Geographic Information Systems, (2010), 270–279. https://doi.org/10.1145/1869790.1869829 |
[2] |
Z. Yang, X. Mu, F. Zhao, Scene classification of remote sensing image based on deep network and multi-scale features fusion, Optik, 171 (2018), 287–293. https://doi.org/10.1016/j.ijleo.2018.06.024 doi: 10.1016/j.ijleo.2018.06.024
![]() |
[3] |
T. Hieu, W. Adrian, Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review, J. Hydro-environ. Res., 37 (2021), 32–45. https://doi.org/10.1016/j.jher.2021.04.003 doi: 10.1016/j.jher.2021.04.003
![]() |
[4] |
G. S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, et al., AID: A benchmark data set for performance evaluation of aerial scene classification, IEEE Trans. Geosci. Remote Sens., 55 (2017), 3965–3981, https://doi.org/10.1109/TGRS.2017.2685945 doi: 10.1109/TGRS.2017.2685945
![]() |
[5] |
R. Cinbis, J. Verbeek, C. Schmid, Approximate fisher kernels of non-iid image models for image categorization, IEEE Trans. Pattern Anal. Mach. Intell., 38 (2016), 1084–1098. https://doi.org/10.1109/TPAMI.2015.2484342 doi: 10.1109/TPAMI.2015.2484342
![]() |
[6] |
F. Hu, G. S. Xia, Z. Wang, X. Huang, L. Zhang; H. Sun, Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 8 (2015), 2015–2030. https://doi.org/10.1109/JSTARS.2015.2444405 doi: 10.1109/JSTARS.2015.2444405
![]() |
[7] |
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60 (2017), 84–90. https://doi.org/10.1145/3065386 doi: 10.1145/3065386
![]() |
[8] | D. Hong, L. Gao, J. Yao, B. Zhang, A. Plaza, J. Chanussot, Graph convolutional networks for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 59 (2021). 5966–5978. https://doi.org/10.1109/TGRS.2020.3015157 |
[9] | K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint, arXiv: 1409.1556. https://doi.org/10.48550/arXiv.1409.1556 |
[10] | C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015), 1–9. |
[11] | K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 770–778. |
[12] |
Y. Guo, J. Ji, X. Lu, H. Huo, T. Fang, D. Li, Global-local attention network for aerial scene classification, IEEE Access, 7 (2019), 67200–67212. https://doi.org/10.1109/ACCES2019.2918732 doi: 10.1109/ACCES2019.2918732
![]() |
[13] |
J. Xie, N. He, L. Fang, A. Plaza, Scale-free convolutional neural network for remote sensing scene classification, IEEE Trans. Geosci. Remote Sens., 57 (2019), 6916–6928. https://doi.org/10.1109/TGRS.2019.2909695 doi: 10.1109/TGRS.2019.2909695
![]() |
[14] |
H. Xie, Y. Chen, P. Ghamisi, Remote sensing image scene classification via label augmentation and intra-class constraint, Remote Sens., 13 (2021), 2566–2586. https://doi.org/10.3390/rs13132566 doi: 10.3390/rs13132566
![]() |
[15] |
N. He, L. Fang, S. Li, J. Plaza, A. Plaza, Skip-connected covariance network for remote sensing scene classification, IEEE Trans. Neural Networks Learn. Syst., 31 (2020), 1461–1474. https://doi.org/10.1109/TNNLS.2019.2920374. doi: 10.1109/TNNLS.2019.2920374
![]() |
[16] | X. Wu, D. Hong, J. Chanussot, Convolutional neural networks for multimodal remote sensing data classification, IEEE Trans. Geosci. Remote Sens., 60 (2022). https://doi.org/10.1109/TGRS.2021.3124913. |
[17] |
Y. Liu, C. Y. Suen, Y. Liu, L. Ding, Scene classification using hierarchical Wasserstein CNN, IEEE Trans. Geosci. Remote Sens., 57 (2019), 2494–2509. https://doi.org/10.1109/TGRS.2018.2873966 doi: 10.1109/TGRS.2018.2873966
![]() |
[18] |
J. Fang, Y. Yuan, X. Lu, Y. Feng, Robust space-frequency joint representation for remote sensing image scene classification, IEEE Trans. Geosci. Remote Sens., 57 (2019), 7492–7502. https://doi.org/10.1109/TGRS.2019.2913816 doi: 10.1109/TGRS.2019.2913816
![]() |
[19] |
H. Sun, S. Li, X. Zheng, X. Lu, Remote sensing scene classification by gated bidirectional network, IEEE Trans. Geosci. Remote Sens., 58 (2019), 82–96. https://doi.org/10.1109/TGRS.2019.2931801 doi: 10.1109/TGRS.2019.2931801
![]() |
[20] |
N. He, L. Fang, S. Li, A. Plaza, J. Plaza, Remote sensing scene classification using multilayer stacked covariance pooling, IEEE Trans. Geosci. Remote Sens., 56 (2018), 6899–6910. https://doi.org/10.1109/TGRS.2018.2845668 doi: 10.1109/TGRS.2018.2845668
![]() |
[21] |
A. Bahri, S. G. Majelan, S. Mohammadi, M. Noori, K. Mohammadi, Remote sensing image classification via improved crossentropy loss and transfer learning strategy based on deep convolutional neural networks, IEEE Geosci. Remote Sens. Lett., 17 (2020), 1087–1091. https://doi.org/10.1109/LGRS.2019.2937872 doi: 10.1109/LGRS.2019.2937872
![]() |
[22] | Q. Wang, S. Liu, J. Chanussot, X. Li, Scene classification with recurrent attention of VHR remote sensing images, IEEE Trans. Geosci. Remote Sens., 57 (2019), 1155–1167. https://doi.org/10/1109/TGRS.2018.2864987 |
[23] |
Y. Chen, Y. Li, H. Mao, X. Chai, L. Jiao, A novel deep nearest neighbor neural network for few-shot remote sensing image scene classification, Remote Sens., 15 (2023), 666–684. https://doi.org/10.3390/rs15030666 doi: 10.3390/rs15030666
![]() |
[24] |
N. Jiang, H. Shi, J. Geng, Multi-scale graph-based feature fusion for few-shot remote sensing image scene classification, Remote Sens., 14 (2022), 5550–5568. https://doi.org/10.3390/rs14215550 doi: 10.3390/rs14215550
![]() |
[25] |
W. Huang, Z. Yuan, A. Yang, C. Tang, X. Luo, TAE-Net: Task-adaptive embedding network for few-shot remote sensing scene classification, Remote Sens., 14 (2022), 111–119. https://doi.org/10.3390/rs14010111 doi: 10.3390/rs14010111
![]() |
[26] |
X. Wu, D. Hong, J. Chanussot, UIU-Net: U-Net in U-Net for infrared small object detection, IEEE Trans. Image Process., 32 (2023), 364–376. https://doi.org/10.1109/TIP.2022.3228497 doi: 10.1109/TIP.2022.3228497
![]() |
[27] |
S. Mei, K. Yan, M. Ma, X. Chen, Q. Du, Remote sensing scene classification using sparse representation-based framework with deep feature fusion, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14 (2021), 5867–5878. https://doi.org/10.1109/JSTARS.2021.3084441 doi: 10.1109/JSTARS.2021.3084441
![]() |
[28] |
Q. Zeng, J. Geng, K. Huang, W. Jiang, J. Guo, Prototype calibration with feature generation for few-shot remote sensing image scene classification, Remote Sens., 13 (2021), 2728–2747. https://doi.org/10.3390/rs13142728 doi: 10.3390/rs13142728
![]() |
[29] | S. Yang, H. Wang, H. Gao, L. Zhang, Feature fusion method based on discriminant correlation analysis for land use classification with few-shot, in proceedings of the International Conference on Computer Engineering and Artificial Intelligence, (2022), 671–675. https://doi.org/10.1109/ICCEAI55464.2022.00143 |
[30] | Q. Chen, Z. Chen, W. Luo, Feature transformation for cross-domain few-shot remote sensing scene classification, preprint, arXiv: 2203.02270. https://doi.org/10.48550/arXiv.2203.02270 |
[31] | H. Y. Tseng, H. Y. Lee, J. B. Huang, M. Yang, Cross-domain few-shot classification via learned feature-wise transformation, preprint, arXiv: 2001.08735. https://doi.org/10.48550/arXiv.2001.08735 |
[32] | W. Chen, Y. Liu, Z. Kira, Y. Wang, J. Huang, A closer look at few-shot classification, preprint, arXiv: 1904.04232. https://doi.org/10.48550/arXiv.1904.04232 |
[33] | A. Chowdhury, M. Jiang, C. Jermaine, Few-shot image classification: Just use a library of pre-trained feature extractors and a simple classifier, in Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021), 9425–9434. |
[34] |
L. Li, J. Han, X. Yao, G. Cheng, L. Guo, DLA-MatchNet for few-shot remote sensing image scene classification, IEEE Trans. Geosci. Remote Sens., 59 (2021), 7844–7853. https://doi.org/10.1109/TGRS.2020.3033336 doi: 10.1109/TGRS.2020.3033336
![]() |
[35] | W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, J. Luo, Revisiting local descriptor based image-to-class measure for few-shot learning, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2019), 7253–7260. |
[36] |
R. Cao, L. Fang, T. Lu, N. He, Self-attention-based deep feature fusion for remote sensing scene classification, IEEE Geosci. Remote Sens. Lett., 18 (2020), 43–47. https://doi.org/10.1109/LGRS.2020.2968550 doi: 10.1109/LGRS.2020.2968550
![]() |
[37] | J. Li, K. Zheng, J. Yao, L.Gao, D. Hong, Deep unsupervised blind hyperspectral and multispectral data fusion, IEEE Geosci. Remote Sens. Lett., 19 (2022). https://doi.org/10.1109/LGRS.2022.3151779 |
[38] |
J. Li, D. Hong, L. Gao, J. Yao, K. Zheng, B. Zhang, et al., Deep learning in multimodal remote sensing data fusion: A comprehensive review, Int. J. Appl. Earth Obs. Geoinf., 112 (2022), 102926. https://doi.org/10.1016/j.jag.2022.102926 doi: 10.1016/j.jag.2022.102926
![]() |
[39] | D. Hong, L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, et al., More diverse means better: multimodal deep learning meets remote-sensing imagery classification, IEEE Trans. Geosci. Remote Sens., 59 (2021). 4340–4354. https://doi.org/10.1109/TGRS.2020.3016820 |
[40] |
S. Chaib, H. Liu, Y. Gu, H. Yao, Deep feature fusion for VHR remote sensing scene classification, IEEE Trans. Geosci. Remote Sens., 55 (2017), 4775–4784. https://doi.org/10.1109/TGRS.2017.2700322 doi: 10.1109/TGRS.2017.2700322
![]() |
[41] | H. Wang, X. Wu, Z. Huang, E. P. Xing, High-frequency component helps explain the generalization of convolutional neural networks, in IEEE Conference on Computer Vision and Pattern Recognition, (2020), 8681–8691. |
[42] |
M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi, Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition, IEEE Trans. Inf. Forensics Secur., 11 (2016), 1984–1996. https://doi.org/10.1109/TIFS.2016.2569061 doi: 10.1109/TIFS.2016.2569061
![]() |
[43] |
C. Chang, C. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., 2 (2011), 1–27. https://doi.org/10.1145/1961189.1961199 doi: 10.1145/1961189.1961199
![]() |
[44] | G. Xia, W. Yang, J. Delon, Y. Gousseau, H. Sun, H. Maître, Structural high-resolution satellite image indexing, in ISPRS TC VⅡ Symposium-100 Years ISPRS, (2010), 298–303. |
[45] | G. Cheng, J. Han, X. Lu, Remote sensing image scene classification: benchmark and state of the art, in Proceedings of the IEEE, 105 (2017), 1865–1883. https://doi.org/10.1109/JPROC.2017.2675998 |
[46] |
H. Li, Z. Cui, Z. Zhu, L. Chen, J. Zhu, H. Huang, et al., RS-MetaNet: Deep metametric learning for few-shot remote sensing scene classification, IEEE Trans. Geosci. Remote Sens., 59 (2020), 6983–6994. https://doi.org/10.1109/TGRS.2020.3027387 doi: 10.1109/TGRS.2020.3027387
![]() |
[47] |
G. Cheng, L. Cai, C. Lang, X, Yao, J, Chen, L. Guo, et al., SPNet: Siamese-prototype network for few-shot remote sensing image scene classification, IEEE Trans. Geosci. Remote Sens., 60 (2022), 5608011. https://doi.org/10.1109/TGRS.2021.3099033 doi: 10.1109/TGRS.2021.3099033
![]() |
1. | Meiling Wang, Xiaohai He, Zhao Zhang, Luping Liu, Linbo Qing, Yan Liu, Dual-process system based on mixed semantic fusion for Chinese medical knowledge-based question answering, 2023, 20, 1551-0018, 4912, 10.3934/mbe.2023228 | |
2. | Sheerin Sitara Noor Mohamed, Kavitha Srinivasan, A comprehensive interpretation for medical VQA: Datasets, techniques, and challenges, 2023, 10641246, 1, 10.3233/JIFS-222569 | |
3. | Anurag Sinha, Vikas Kumar, Vandana Sharma, Ahmed Alkhayyat, 2023, QML-FFSD: A Novel Approach for Early Detection of SCDs through Feature Fusion of Antibiotics Composition and Symptoms Data using Quantum ML, 979-8-3503-3179-0, 1, 10.1109/GlobConET56651.2023.10150112 | |
4. | Ruoqi Zhang, Xiaoming Huang, Qiang Zhu, Weakly supervised salient object detection via image category annotation, 2023, 20, 1551-0018, 21359, 10.3934/mbe.2023945 | |
5. | Qian Yang, Lijun Liu, Xiaobing Yang, Li Liu, Wei Peng, 2024, Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering, 979-8-3503-8622-6, 1296, 10.1109/BIBM62325.2024.10821854 | |
6. | Wei Bi, Qingzhen Xiong, Xingyi Chen, Qingkun Du, Jun Wu, Zhaoyu Zhuang, Intelligent visual question answering in TCM education: An innovative application of IoT and multimodal fusion, 2025, 118, 11100168, 325, 10.1016/j.aej.2024.12.052 |