Domain Adaptation Based on Correlation Subspace Dynamic Distribution Alignment for Remote Sensing Image Scene Classification

Zhang, Jun; Liu, Jiao; Pan, Bin; Shi, Zhenwei

doi:10.1109/tgrs.2020.2985072

Cited by 76 publications

(31 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, many deep convolutional neural network (DCNN)-based models have been proposed [13][14][15][16], replacing scene classification based on handcrafted-features such as color histograms [17], scale-invariant feature transform (SIFT) [18], histogram of oriented gradients (HOG) [19], and global image descriptor (GIST) [20]. These models can achieve better classification performance due to the powerful feature representation and generalization ability of pre-trained DCNN models (e.g., AlexNet [21], VGG-VD16 [22], GoogLeNet [23], and ResNet [24]) on the ImageNet [25].…”

Section: Introductionmentioning

confidence: 99%

Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks

Yang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invariance DCNN model that is achieved by directly enforcing the labels of the training samples before and after rotating to be mapped close to each other. In addition to the cross-entropy cost function for the traditional CNN models, we impose a rotation invariance regularization constraint on the objective function of our proposed model. The experimental results obtained using three publicly-available scene classification datasets show that the proposed method can generally improve the classification performance by 2~3% and achieves satisfactory classification performance compared with some state-of-the-art methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks

Yang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The sea-land segmentation methods based on machine learning can achieve high automation, but it requires a combination of multiple machine learning methods to obtain better extraction results. Deep learning based on fully convolutional neural network has achieved satisfactory performance in the field of semantic segmentation [14]- [19]. Fully convolutional network can automatically extract the features from input images and reconstruct the image resolution through the decoder [14].…”

Section: Introductionmentioning

confidence: 99%

SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning

Cui

Wei

Ling

et al. 2021

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

View full text Add to dashboard Cite

Sea-land segmentation of remote sensing images is of great significance to the dynamic monitoring of coastlines. However, the types of objects in the coastal zone are complex, and their spectra, textures, shapes, and distribution features are different. Therefore, sea-land segmentation for various types of coastlines is still a challenging task. In this paper, a scale-adaptive semantic segmentation network, called SANet, is proposed for sea-land segmentation of remote sensing images. SANet has made two innovations on the basis of the classic encoder-decoder structure. First, to integrate the spectral, textural and semantic features of ground objects at different scales, we designed an adaptive multi-scale feature learning module (AML) to replace the conventional serial convolution operation. The AML module mainly contains a multi-scale feature extraction unit and an adaptive feature fusion unit. The former can capture the multiscale detailed information and contextual semantic information of objects from an early stage, while the latter can adaptively fuse feature maps of different scales. Second, we adopted the squeezeand-excitation module to bridge the corresponding layers of the codec so that SANet can selectively emphasize the features of the weak sea-land boundaries. Experiments on a set of Gaofen-1 remote sensing images demonstrated that SANet achieved more accurate segmentation results and obtained sharper boundaries than other methods for various natural and artificial coastlines.

show abstract

“…Most of the existing methods try to align the marginal distribution [14], [15], or the conditional distribution [16], or assume that both distributions are equally important [17]. In the field of computer vision, the latest research has shown that perform dynamic distribution adaptation (DDA) can obtain better transfer performance [18]. Wang et al [19] first proposed Dynamic Distribution Adaptation Network (DDAN) to use the deep neural network in learning end-toend transfer classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source domain) and testing (target domain) data sets obey the same feature distribution; 2) Sufficient labeled data with fault information is available for model training. In real industrial scenarios, especially for different machines, these assumptions are mostly invalid, which makes it a huge challenge to build reliable diagnostic model. Motivated by transfer learning, we present a novel intelligent method named deep transfer network (DTN) with multi-kernel dynamic distribution adaptation (MDDA) to address the problem of cross-machine fault diagnosis. In the proposed approach, the DTN has wide first-layer convolutional kernel and several small convolutional layers, which is utilized to extract transferable features across different machines and suppress high frequency noise. Then, the MDDA method constructs a weighted mixed kernel function to map different transferable features to a unified feature space, and the relative importance of the marginal and conditional distributions are also evaluated dynamically. The proposed method is verified by three transfer learning tasks of bearings, in which the health states of wind turbine bearings in real scenario are identified by using diagnosis knowledge from two different bearings in laboratories. The results show that the proposed method can achieve higher diagnosis accuracy and better transfer performance even under different noisy environment conditions than many other state-of-the-art methods. The presented framework offers a promising approach for cross-machine fault diagnosis. INDEX TERMS Deep transfer network, multi-kernel dynamic distribution adaptation, cross-machine fault diagnosis, transfer learning, bearings.

show abstract

Domain Adaptation Based on Correlation Subspace Dynamic Distribution Alignment for Remote Sensing Image Scene Classification

Cited by 76 publications

References 45 publications

Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks

Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks

SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning

Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

Contact Info

Product

Resources

About