Hierarchical Recognition System for Target Recognition from Sparse Representations

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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“…Cui et al [47] introduce DBN to SAR ATR, where stacked RBM are used to extract features and then fed to trainable classifier.…”

Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,339

1,204

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“…Recently, feature fusion based on a deep neural networks has performed excellently in various fields [ 12 , 13 , 14 ], which has prompted scholars to conduct research on SAR imagery target recognition with neural networks [ 15 , 16 , 17 ]. However, the remarkable performance of a deep neural network requires a large number of tagged data as training samples, which is hard to accomplish in SAR target recognition.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

Kang

Leng

et al. 2017

Sensors

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Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.

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“…In the 3D-SCM method, the 3D scattering center model, established offline from the CAD model of the target, was employed to predict the 2D scattering centers for template matching. The DL networks for comparison were composed of the restricted RBM (RRBM) [56], the CNN with DA (DA-CNN) [57] and additional data generated by image processing methods, the CNN with SVM (CNN+SVM) [37], the A-Convnet [57] that replaced the fully connected layers with a convolution layer in a CNN, the sparse AE pre-trained CNN (AE-CNN) [58] where the convolution kernel was trained on randomly sampled image patches using unsupervised sparse auto-encoder, the ED-AE [30], and the Triplet-DAE [31]. Among these methods, the CNN with SVM and the A-Convnet were implemented in our codes with Python.…”

Section: Evaluation On Three-target Classificationmentioning

confidence: 99%

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

Tian

Lin

Gao

et al. 2020

Sensors

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Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks.

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Hierarchical Recognition System for Target Recognition from Sparse Representations

Cited by 15 publications

References 25 publications

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

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