Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search

Feng, Jie; Wu, Xiande; Shang, Ronghua; Sui, Chenhong; Li, Jie; Jiao, Licheng; Zhang, Xiangrong

doi:10.1109/tgrs.2020.3011943

Cited by 62 publications

(15 citation statements)

References 59 publications

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“…Its potential to enhance mostly CNN-based methods has been reported [32]. In addition, it has been used in conjunction with recurrent neural network models [33][34][35][36], and graph neural networks [37,38]. The main idea behind the attention mechanism is to give different weights to different information.…”

Section: Attention Mechanism In Deep Learningmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Attention Mechanism In Deep Learningmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…To deal with this problem, people try to use different convolution kernels according to different features. Jie Feng et al [42]adopted different sizes and locations of spatial windows according to sample-specific distribution, which brings new ideas to the classification of CNN-based methods.…”

Section: A Deep-learning-based Hyperspectral Image Classificationmentioning

confidence: 99%

Multiscale Graph Sample and Aggregate Network With Context-Aware Learning for Hyperspectral Image Classification

Ding

Zhao

Zhang

et al. 2021

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

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“…LSTM is a special structure of RNN and it is capable to learn long-term dependencies and deal with the gradient vanishing or the exploding problems present in the traditional RNN [35,36]. It works well to solve a large variety of problems in the temporal or spectral domains, and is successfully used for HS classification.…”

Section: A Lstmmentioning

confidence: 99%

A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification

Zheng¹,

Liu²,

Du³

et al. 2021

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

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High-resolution (HR) satellite images, due to the technical constraints on spectral and spatial resolutions, usually contain only several broad spectral bands but with a very high spatial resolution. This provides rich spatial details of the objects on the Earth surface, while their spectral discrimination is relatively low. Recently, the increase of the satellite revisit times made it possible to acquire more frequent data coverage for finer classification. In this paper, we proposed a novel multitemporal deep fusion network (MDFN) for short-term multitemporal HR images classification. Specifically, a two-branch structure of MDFN is designed, which includes a long short-term memory (LSTM) and a convolutional neural network (CNN). The LSTM branch is mainly used to learn the joint expression of different temporal-spectral features. For the CNN branch, the 3D convolution is firstly applied along the temporal and spectral dimensions to jointly learn the temporal-spatial and spectral-spatial information, respectively, and then the 2D convolution is performed along the spatial dimension to further extract the spatial context information. Finally, features generated from the two different branches are fused to obtain the discriminative high-level semantic information for classification. Experimental results carried on two real multitemporal HR remote sensing data sets demonstrate that the proposed MDFN provides better classification performance over the state-of-the-art methods, and it also shows the potentiality to use short-term multitemporal HR images for more accurate Land Use/Land Cover (LULC) mapping.

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Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search

Cited by 62 publications

References 59 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Multiscale Graph Sample and Aggregate Network With Context-Aware Learning for Hyperspectral Image Classification

A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification

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