Feature extraction using PCA for VHR satellite image time series spatio-temporal classification

Réjichi, Safa; Chaâbane, F.

doi:10.1109/igarss.2015.7325806

Cited by 19 publications

(14 citation statements)

References 6 publications

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“…In the training stage, we first upscale low-resolution to the high-resolution size by the bicubic interpolation, then extract features of up-scaled image. A classical feature extraction strategy in image restoration is to densely extract patches and then represent them by a set of pre-trained bases such as PCA (21), DCT (22), Haar (23), etc. This is equivalent to convolving the image by a set of filters, each of which is a basis.…”

Section: Methodsmentioning

confidence: 99%

High-resolution CT image retrieval using sparse convolutional neural network

Lei

Zhou

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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We propose a high-resolution CT image retrieval method based on sparse convolutional neural network. The proposed framework is used to train the end-to-end mapping from low-resolution to high-resolution images. The patch-wise feature of low-resolution CT is extracted and sparsely represented by a convolutional layer and a learned iterative shrinkage threshold framework, respectively. Restricted linear unit is utilized to non-linearly map the low-resolution sparse coefficients to the high-resolution ones. An adaptive high-resolution dictionary is applied to construct the informative signature which is highly connected to a high-resolution patch. Finally, we feed the signature to a convolutional layer to reconstruct the predicted high-resolution patches and average these overlapping patches to generate high-resolution CT. The loss function between reconstructed images and the corresponding ground truth high-resolution images is applied to optimize the parameters of end-to-end neural network. The well-trained map is used to generate the high-resolution CT from a new low-resolution input. This technique was tested with brain and lung CT images and the image quality was assessed using the corresponding CT images. Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean absolute error (MAE) indexes were used to quantify the differences between the generated high-resolution and corresponding ground truth CT images. The experimental results showed the proposed method could enhance images resolution from low-resolution images. The proposed method has great potential in improving radiation dose calculation and delivery accuracy and decreasing CT radiation exposure of patients.

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Section: Methodsmentioning

confidence: 99%

High-resolution CT image retrieval using sparse convolutional neural network

Lei

Zhou

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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“…However, its performance still needs to be improved. Réjichi S et al [18] used principal component analysis to preserve the main information of the image and reduce the feature's dimension. This method can obtain the invariant features in the classification task, which improves the efficiency of classification.…”

Section: Introductionmentioning

confidence: 99%

MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images

Wang

Jiang

et al. 2019

Applied Sciences

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Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

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“…This provides an effective nondestructive means for underwater measurement in various scenarios [ 22 ]. In reference [ 23 ], a lithium polymer battery of 10,000 mAh capacity is used for the camera battery and the camera can work for up to 10 days, if it is controlled to record 60 seconds of video every two-hours under the sea with a depth between 1000 m and 1800 m. Traditional image classification methods include the BP neural network [ 24 ], support vector machine (SVM) [ 25 ], and principal component analysis [ 26 ], etc. The BP neural network and SVM require a great number of parameters when the dimension of the input is large.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

Zheng

Wang

Xin

et al. 2019

Sensors

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The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

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Feature extraction using PCA for VHR satellite image time series spatio-temporal classification

Cited by 19 publications

References 6 publications

High-resolution CT image retrieval using sparse convolutional neural network

High-resolution CT image retrieval using sparse convolutional neural network

MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images

A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

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