A Convolutional Neural Network Based on Grouping Structure for Scene Classification

Wu, Xuan; Zhang, Zhijie; Zhang, Wanchang; Yi, Ya Sha; Zhang, Chuanrong; Xu, Qiang

doi:10.3390/rs13132457

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…The emergence of deep learning 6 – 8 has provided an automated framework for feature extraction and representation, including classification and targets detection 9 – 12 . Currently, widely used deep learning detection algorithms can be mainly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

Ma,

Liu,

Huang

et al. 2023

Sci Rep

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This study aims to develop a deep learning model to improve the accuracy of identifying tiny targets on high resolution remote sensing (HRS) images. We propose a novel multi-level weighted depth perception network, which we refer to as MwdpNet, to better capture feature information of tiny targets in HRS images. In our method, we introduce a new group residual structure, S-Darknet53, as the backbone network of our proposed MwdpNet, and propose a multi-level feature weighted fusion strategy that fully utilizes shallow feature information to improve detection performance, particularly for tiny targets. To fully describe the high-level semantic information of the image, achieving better classification performance, we design a depth perception module (DPModule). Following this step, the channel attention guidance module (CAGM) is proposed to obtain attention feature maps for each scale, enhancing the recall rate of tiny targets and generating candidate regions more efficiently. Finally, we create four datasets of tiny targets and conduct comparative experiments on them. The results demonstrate that the mean Average Precision (mAP) of our proposed MwdpNet on the four datasets achieve 87.0%, 89.2%, 78.3%, and 76.0%, respectively, outperforming nine mainstream object detection algorithms. Our proposed approach provides an effective means and strategy for detecting tiny targets on HRS images.

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Section: Introductionmentioning

confidence: 99%

MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

Ma,

Liu,

Huang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The deep learning-based detection method is mainly divided into single-stage algorithms (such as you only look once (YOLO) (Redmon et al, 2016), single-shot multi-box detector [Souaidi and Ansari, 2022), and RetinaNet (Miao et al, 2022)], and two-stage algorithms [including the region convolutional neural network (R-CNN) (Wu et al, 2021), Fast R-CNN (Girshick, 2015), and Faster R-CNN (Ren et al, 2017)]. It is known that the single-stage algorithms can focus more on the fusion and prediction of the target features at different scales, which can be extended to the multiscale ship detection in the SAR images.…”

Section: Introductionmentioning

confidence: 99%

Lightweight and anchor-free frame detection strategy based on improved CenterNet for multiscale ships in SAR images

Xie

Jiang²,

Zhang³

et al. 2022

Front. Comput. Sci.

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Ship detection using synthetic aperture radar (SAR) images has important applications in military and civilian fields, but the different sizes of the ship downgrade the detection accuracy of multiscale ships. Aiming at the problem of the poor accuracy and low efficiency of multiscale ship detection in complex scenes, this paper proposes a lightweight and anchor-free frame detection strategy for multiscale ships in SAR images. First, to deal with the problems of limited training samples, different sizes, attitudes, and angles of the ships in SAR images, a data augmentation strategy suitable for SAR images is adopted to expand the training space, followed by multiscale training to enhance the model generalization ability for multiscale ship detection. Second, a lightweight and anchor-free ship detection model based on the improved CenterNet is proposed, which abandons the dense anchor frame generation and extracts the key point of the ships for detection and positioning. Compared with the anchor frame-based detection method, this proposed detection model does not need to use the post-processing method to remove redundant anchor frames, and can accurately locate the center point of the ships with a better detection performance. Third, to reduce the model size and simplify the model parameters, a more lightweight network design is adopted in combination with the characteristics of SAR images. Hence, a residual network (ResNet) with fewer convolutional layers is constructed as the backbone network, and the cross-stage partial network (CSPNet) and spatial pyramid pooling (SPP) network are designed as the bottleneck network. The shallow ResNet can fully extract the SAR image features and reduce the training overfitting, and CSPNet and SPP can effectively combine the low-level image features to obtain the high-level features, reducing the model computation while at the same time enhancing the feature extraction ability. Finally, the evaluation index of the common objects in the context dataset is introduced, which can provide higher-quality evaluation results for ship detection accuracy and provide comprehensive evaluation indicators for multiscale ship detection. Experimental results show that the proposed strategy has the advantages of high detection efficiency, strong detection ability, and good generalization performance, which can achieve real-time and high-precision detection of the multiscale ship in complex SAR images.

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“…Bi et al [30] used an attention mechanism to enhance the ability to extract local semantic information from remote sensing scenes. Wu et al [31] proposed a revolutionary neural network framework based on a group revolution scheme, which improves the efficiency of CNN. Xie et al [32] proposed label augmentation to expand the remote sensing scene dataset, which realizes the classification of few-shot remote sensing scenes.…”

Section: Introductionmentioning

confidence: 99%

A Deformable Convolutional Neural Network with Spatial-Channel Attention for Remote Sensing Scene Classification

Wang

Lan

2021

Remote Sensing

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Remote sensing scene classification converts remote sensing images into classification information to support high-level applications, so it is a fundamental problem in the field of remote sensing. In recent years, many convolutional neural network (CNN)-based methods have achieved impressive results in remote sensing scene classification, but they have two problems in extracting remote sensing scene features: (1) fixed-shape convolutional kernels cannot effectively extract features from remote sensing scenes with complex shapes and diverse distributions; (2) the features extracted by CNN contain a large number of redundant and invalid information. To solve these problems, this paper constructs a deformable convolutional neural network to adapt the convolutional sampling positions to the shape of objects in the remote sensing scene. Meanwhile, the spatial and channel attention mechanisms are used to focus on the effective features while suppressing the invalid ones. The experimental results indicate that the proposed method is competitive to the state-of-the-art methods on three remote sensing scene classification datasets (UCM, NWPU, and AID).

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A Convolutional Neural Network Based on Grouping Structure for Scene Classification

Cited by 12 publications

References 42 publications

MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

Lightweight and anchor-free frame detection strategy based on improved CenterNet for multiscale ships in SAR images

A Deformable Convolutional Neural Network with Spatial-Channel Attention for Remote Sensing Scene Classification

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