Deep learning in multimodal remote sensing data fusion: A comprehensive review
TLDR
In this paper , a literature survey is conducted to analyze the trends of multimodal remote sensing data fusion, and some prevalent sub-fields in the field are reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radaroptical and RS-Geospatial Big Data fusion.About:
This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2022-08-01 and is currently open access. It has received 12 citations till now. The article focuses on the topics: Sensor fusion & Computer science.read more
Citations
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Multimodal Attention-Aware Convolutional Neural Networks for Classification of Hyperspectral and LiDAR Data
TL;DR: Li et al. as discussed by the authors proposed the convolution neural network method with the attention mechanism to enhance the feature extraction of light detection and ranging (LiDAR) data, and their elaborately designed cascaded block contains a short path architecture beneficial for multistage information exchange.
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Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection
TL;DR: In this paper , a frequency-to-spectrum mapping generative adversarial network (FTSGAN) is proposed for hyperspectral anomaly detection, where the depth separable features of background and anomalies are enhanced in the FrFD.
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Deep transformer and few‐shot learning for hyperspectral image classification
TL;DR: In this paper , a novel deep transformer and few-shot learning (DT-FSL) classification framework is proposed, attempting to realize fine-grained classification of hyperspectral image (HSI) with only a fewshot instances.
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Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review
TL;DR: A comprehensive overview of tensor decomposition-based hyperspectral data processing methods can be found in this paper , with a pivotal description of the existing methodologies and a representative exhibition of experimental results.
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Fine-Scale Urban Informal Settlements Mapping by Fusing Remote Sensing Images and Building Data via a Transformer-Based Multimodal Fusion Network
TL;DR: Fan et al. as mentioned in this paper proposed a UIS semantic segmentation method, namely UisNet, that utilizes a transformer-based block to receive multimodal data, including high-spatial-resolution remote sensing images (parcel- and pixel-level) and building polygon data (object-level).
References
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Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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A survey of deep neural network architectures and their applications
TL;DR: This work was supported in part by the Royal Society of the UK, the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany.
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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
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Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Liangpei Zhang,Lefei Zhang,Bo Du +2 more
TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
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A tutorial on synthetic aperture radar
Alberto Moreira,Pau Prats-Iraola,Marwan Younis,Gerhard Krieger,Irena Hajnsek,Konstantinos Papathanassiou +5 more
TL;DR: This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometric as well as of emerging techniques (e.g., polarimetric SARinterferometry, tomography and holographic tomography).