MINet: Multilevel Inheritance Network-Based Aerial Scene Classification

Hu, Jian; Shu, Qidi; Pan, Jun; Tu, Jianguang; Zhu, Ying; Wang, Mi

doi:10.1109/lgrs.2021.3088277

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The two branches are merged with depthwise convolutions to decrease the dimensionality. Hu et al [108] introduced a multilevel inheritance network (MINet), where FPN based on ResNet-50 is adopted to acquire multilayer features. Subsequently, an attention mechanism is employed to augment the expressive capacity of features at each level.…”

Section: Pretrained Cnns For Feature Extractionmentioning

confidence: 99%

“…It is also noticed that [83,106] aims to solve multiple research problems. Combine GIST [156] on CNN Integrate multiple color features [157] Deep color network fusion Combine mid-level and deep-level information [158] Merge deep level feature and mid-level feature of encoder in decoder branch Extract multilevel feature maps [108] Multilayer network Improve performance in classifier learning [159] Statistical transfer via inter-class similarity…”

Section: Research Problem and Utilized Research Techniquesmentioning

confidence: 99%

“…This approach has the ability to focus on key features or regions within an image to capture relevant information. During our meta-analysis, attentionbased CNNs for remote sensing scene classification [100,[103][104][105][106][107][108]143] have demonstrated promising performance. CBAM+RDN [100] obtained the highest accuracy of 99.08% on the AID dataset.…”

Section: Shift Of Paradigm In Scene Classification Architecturesmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Thapa,

Horanont,

Neupane

et al. 2023

Remote Sensing

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Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.

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Section: Pretrained Cnns For Feature Extractionmentioning

confidence: 99%

Section: Research Problem and Utilized Research Techniquesmentioning

confidence: 99%

Section: Shift Of Paradigm In Scene Classification Architecturesmentioning

confidence: 99%

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Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Thapa,

Horanont,

Neupane

et al. 2023

Remote Sensing

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show abstract

“…The results show that our method outperforms fine‐tuning VGG‐19 on the NaSC‐TG2 dataset (training rate of 20%) and an overall accuracy improvement of 0.89%. Compared with fine‐tuning VGG‐19, GBNet [27], and MINet [28], the overall accuracy of VGG‐SA on the WHU‐RS19 dataset with a 40% training ratio improved by 0.52%, 2.32%, and 0.13%, respectively. The proposed method yields higher accuracy than state‐of‐the‐art methods on the AID dataset with a training ratio of 20%, which improves by 1.35%, 3.26%, and 0.17% compared with fine‐tuning VGG‐19, VGG‐VD16+SAFF [2], and MG‐CAP (Sqrt‐E) [29], respectively.…”

Section: Introductionmentioning

confidence: 99%

Scene classification for remote sensing images with self‐attention augmented CNN

Liu

Dong

et al. 2022

IET Image Processing

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Remote sensing scene classification aims to automatically assign a specific semantic label to each image. It is challenging to classify remote sensing scene images due to the images' diversity and rich spatial information. Recently, convolutional neural networks have been widely used to overcome these difficulties, such as the famous Visual Geometry Group (VGG) network. However, the VGG network with local receptive fields cannot model the global information of remote sensing images well. It also needs a large number of parameters and floating point operations to achieve satisfactory accuracy. To overcome these challenges, we introduce the self-attention mechanism to the VGG network. Specifically, we replace the last four convolutional layers in the VGG-19 network with two cascaded self-attention blocks, each consisting of two multi-head self-attention (MHSA) layers with the residual network structure. The new structure can simultaneously explore the local and global information from remote sensing scenes. Such improvements not only reduce model parameters but also improve the classification performance. The effectiveness of the proposed method is validated through experiments on four public data sets, i.e., NaSC-TG2, WHU-RS19, AID and EuroSAT.

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Multilayer Feature Fusion Network With Spatial Attention and Gated Mechanism for Remote Sensing Scene Classification

Meng

Zhao

Zhang

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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MINet: Multilevel Inheritance Network-Based Aerial Scene Classification

Cited by 6 publications

References 27 publications

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Scene classification for remote sensing images with self‐attention augmented CNN

Multilayer Feature Fusion Network With Spatial Attention and Gated Mechanism for Remote Sensing Scene Classification

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