Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Gu, Songwei; Zhang, Rui; Luo, Hongxia; Li, Mengyao; Feng, Huamei; Tang, Xuguang

doi:10.3390/rs13091713

Cited by 19 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter automatically extracts remote sensing image features through deep learning methods such as the autoencoder Remote Sens. 2023, 15, 2212 2 of 27 (AE) [23][24][25], CNN [26], and generative adversarial network (GAN) [27][28][29]. The traditional methods need to specially design a feature extraction operator for the remote sensing image.…”

Section: Introductionmentioning

confidence: 99%

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

Feng

Sheng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

Feng

Sheng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Mei et al [45] proposed a multi-level features fusion framework based on sparse representation. Gu et al [46] proposed a generative adversarial networks (GANs) structure with a pyramidal multiscale structure. They achieve good classification results by multiscale feature fusion, multibranch fusion, or loss function design.…”

Section: Introductionmentioning

confidence: 99%

Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes

Wang

Zhou

et al. 2023

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

View full text Add to dashboard Cite

Monitoring the land covers in complex landscapes is of great significance for the sustainable development of mine geoenvironments. As most existing remote sensing scene datasets are composed of RGB images, there is a lack of multimodal datasets for complex landscapes with mining land covers (MLCs) at a finescale. In this study, a new dataset was created by the China University of Geosciences (CUG), Wuhan (named CUG-MLCs) using ZiYuan-3 imagery-based multispectral and topographic data. Moreover, the characteristics of multi-size objects, irregular or blurred edges, and spectral-spatial-topographic heterogeneity and variability limited the classification accuracy. Therefore, an edge enhanced channel attention-based graph convolution network (ECA-GCN) was proposed and tested. The proposed ECA-GCN includes three key modules: (1) Multiscale and shallow feature fusion, used to fuse the multiscale convolutional features and shallow features, which helps present the MLC features with various scales; (2) edge enhanced channel attention, used to further select effective channels after a spatial edge feature enhancement, which helps identify irregular or blurred MLCs; and (3) edge detection-based GCN, used for edge feature-based adjacency matrix and feature maps from (2) to construct GCN, which can obtain edge node relation and global contextual information. This framework improved the representation of complex landscape characteristics. The proposed ECA-GCN achieved an overall accuracy of 66.60% ± 1.39%, averaged accuracy of 36.25% ± 1.50%, and Kappa of 55.91% ± 2.05%, thus, outperforming other models. In general, the proposed dataset and model were positive for the fine classification of complex landscapes.

show abstract

“…Remote sensing images usually contain feature information with a large amount of texture and structure information, which is complex [23], and existing natural image generation models rarely consider the structure information in the generation process. Therefore, if they are used in remote sensing image generation, they will lead to geometric structure distortion in the generated sample images, and the generated pseudo-sample images are often poorly realistic and insufficiently diverse to be reliably used as the basis for various analyses and applications in remote sensing [24]. Additionally, almost all existing related studies in the direction of remote sensing are focused on tasks such as image classification and segmentation, and there are few studies on object detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

Zhu

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

A rich and effective dataset is an important foundation for the development of AI algorithms, and the quantity and quality of the dataset determine the upper limit level of the algorithms. For aerospace remote sensing datasets, due to the high cost of data collection and susceptibility to meteorological and airway conditions, the existing datasets have two problems: firstly, the number of datasets is obviously insufficient, and, secondly, there is large unevenness between different categories in datasets. One of the effective solutions is to use neural networks to generate fake data by learning from real data, but existing methods still find difficulty in generating remote sensing sample images with good texture detail and geometric distortion. To address the shortcomings of existing image generation algorithms, this paper proposes a gradient structure information-guided attention generative adversarial network (SGA-GAN) for remote sensing image generation, which contains two innovative initiatives: on the one hand, a learnable gradient structure information extraction branch network can be added to the generator network to obtain complex structural information in the sample image, thus alleviating the distortion of the sample geometric structure in remote sensing image generation; on the other hand, a multidimensional self-attention feature selection module is proposed to further improve the quality of the generated remote sensing images by connecting cross-attentive modules as well as spatial and channel attention modules in series to guide the generator to better utilize global information. The algorithm proposed in this paper outperformed other methods, such as StyleGAN-XL and FastGAN, in both the qualitative and quantitative evaluation, whereby the FID on the DOTA dataset decreased by 23.927 and the IS was improved by 2.351. The comparison experiments show that the method proposed in this paper can generate more realistic sample images, and images generated by this method can improve object detection metrics by increasing the number of single-category datasets and the number of targets in fewer categories in multi-category datasets, which means it can be effectively used in the field of intelligent processing of remote sensing images.

show abstract

Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Cited by 19 publications

References 35 publications

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes

Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

Contact Info

Product

Resources

About