RADC-Net: A residual attention based convolution network for aerial scene classification

Bi, Qi; Qin, Kun; Zhang, Han; Li, Zhili; Xu, Kai

doi:10.1016/j.neucom.2019.11.068

Cited by 68 publications

(48 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the training reconstruction phase, Goh et al [130] used the mechanism of top-down attention in deep Boltzmann machines (DBMs) as a regularizing factor. Note that the network can be globally optimized using a top-down learning strategy in a similar manner, where the maps progressively output to the input throughout the learning process [129][130][131][132].…”

Section: Residual Attention Neural Networkmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

View full text Add to dashboard Cite

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

show abstract

Section: Residual Attention Neural Networkmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Meanwhile, in [17], texture information of images obtained using Local Binary Pattern (LBP) was used in addition to traditional RGB images for training deep learning models. On the contrary, in [8], a novel network architecture was proposed for aerial image classification, that consisted of dense blocks with addition and modification of residual attention layers and a classification layer. The network was designed to contain fewer parameters than the traditional models.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Consequently, CNN models have been applied for aerial image classification also, leading to better classification performance compared to the prior approaches. Most [6,7,8]. The training phase involved in these approaches is computationally very expensive.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Relu-Based Feature Fusion For Aerial Scene Classification

Arefeen

Nimi

Uddin

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.

show abstract

“…This model may have an advantage when the data set's scale increases. In [60], a self-attention- [10] 96.90 (0.77) AlexNet+SPP [17] 95.95 (1.01) CCP-net [18] 97.52 (0.97) AlexNet+MSCP [56] 97.29 (0.63) VGG-VD16+MSCP [56] 98.36 (0.58) BAFF [57] 95.48 (0.22) RADC-Net [58] 97.05 (0.48) D-CapsNet [54] 99.05 (0.12) DDRL-AM [59] 99.05 (0.08) VGG-VD16+CapsNet [55] 98.81 (0.12) AlexNet+SAFF [60] 96.13 ( 0.97) VGG-VD16+SAFF [60] 97.02 ( 0.78) [18], a concentric circle pooling layer is proposed to incorporate rotation-invariant spatial layout information of remote sensing scene images.…”

Section: E Compared With Other Pre-trained Cnn-based Methodsmentioning

confidence: 99%

Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification

Yang

et al. 2021

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

View full text Add to dashboard Cite

Remote sensing imagery typically comprises successive background contexts and complex objects. Global average pooling is a popular choice to connect the convolutional and fullyconnected (FC) layers for the deep convolution network. This paper equips the networks with another pooling strategy, namely the deep object-centric pooling (DOCP), to pool convolutional features considering the location of an object within the scene image. The proposed DOCP network structure consists of two steps: inferring object's location and separately pooling the foreground and background features to generate an object-level representation. Specifically, a spatial context module is presented to learn the location of the object of interest in the scene image. Then, the convolutional feature maps are pooled separately in the foreground and background of the object. Finally, the FC layer concatenates these pooled features and is followed by a batch normalization layer, a dropout layer, and a softmax layer. Two challenging data sets are employed to validate our approach. The experimental results demonstrate that the proposed DOCPnet can outperform the corresponding pooling methods and achieve better classification performance than other pre-trained convolutional neural network-based scene classification methods.

show abstract

RADC-Net: A residual attention based convolution network for aerial scene classification

Cited by 68 publications

References 35 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

A Lightweight Relu-Based Feature Fusion For Aerial Scene Classification

Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification

Contact Info

Product

Resources

About