다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage 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.

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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Automatic image registration is a vital yet challenging task, particularly for remote sensing images. A fully automatic registration approach which is accurate, robust, and fast is required. For this purpose, a novel coarse-to-fine scheme for automatic image registration is proposed in this paper. This scheme consists of a preregistration process (coarse registration) and a fine-tuning process (fine registration). To begin with, the preregistration process is implemented by the scale-invariant feature transform approach equipped with a reliable outlier removal procedure. The coarse results provide a near-optimal initial solution for the optimizer in the fine-tuning process. Next, the fine-tuning process is implemented by the maximization of mutual information using a modified Marquardt-Levenberg search strategy in a multiresolution framework. The proposed algorithm is tested on various remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal) with the affine transformation model. The experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm.

A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information

We propose an effective deep neural network aiming at remote sensing image registration problem. Unlike conventional methods doing feature extraction and feature matching separately, we pair patches from sensed and reference images, and then learn the mapping directly between these patch-pairs and their matching labels for later registration. This end-to-end architecture allows us to optimize the whole processing (learning mapping function) through information feedback when training the network, which is lacking in conventional methods. In addition, to alleviate the small data issue of remote sensing images for training, our proposal introduces a self-learning by learning the mapping function using images and their transformed copies. Moreover, we apply a transfer learning to reduce the huge computation cost in the training stage. It does not only speed up our framework, but also get extra performance gains. The comprehensive experiments conducted on seven sets of remote sensing images, acquired by Radarsat, SPOT and Landsat, show that our proposal improves the registration accuracy up to 2.4–53.7%.

A deep learning framework for remote sensing image registration

Multitemporal synthetic aperture radar (SAR) images have been successfully used for the detection of different types of terrain changes. SAR image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a difference image. In order to obtain a better difference map, image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio difference image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused difference image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.

Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection

In this work, we propose a novel tracking algorithm with real-time performance based on the ‘Actor-Critic’ framework. This framework consists of two major components: ‘Actor’ and ‘Critic’. The ‘Actor’ model aims to infer the optimal choice in a continuous action space, which directly makes the tracker move the bounding box to the object’s location in the current frame. For offline training, the ‘Critic’ model is introduced to form a ‘Actor-Critic’ framework with reinforcement learning and outputs a Q-value to guide the learning process of both ‘Actor’ and ‘Critic’ deep networks. Then, we modify the original deep deterministic policy gradient algorithm to effectively train our ‘Actor-Critic’ model for the tracking task. For online tracking, the ‘Actor’ model provides a dynamic search strategy to locate the tracked object efficiently and the ‘Critic’ model acts as a verification module to make our tracker more robust. To the best of our knowledge, this work is the first attempt to exploit the continuous action and ‘Actor-Critic’ framework for visual tracking. Extensive experimental results on popular benchmarks demonstrate that the proposed tracker performs favorably against many state-of-the-art methods, with real-time performance.

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Real-time 'Actor-Critic' Tracking

In order to solve the image segmentation problem which assigns a label to every pixel in an image such that pixels with the same label share certain visual characteristics more effectively, a novel approach based on memetic algorithm (MISA) is proposed. Watershed segmentation is applied to segment original images into non-overlap small regions before performing the portioning process by MISA. MISA adopts a straightforward representation method to find the optimal combination of watershed regions under the criteria of interclass variance in feature space. After implementing cluster-based crossover and mutation, an individual learning procedure moves exocentric regions in current cluster to the one they should belong to according to the distance between these regions and cluster centers in feature space. In order to evaluate the new algorithm, six texture images, three remote sensing images and three natural images are employed in experiments. The experimental results show that MISA outperforms its genetic version, the Fuzzy c-means algorithm, and K-means algorithm in partitioning most of the test problems, and is an effective approach when compared with two state-ofthe-art image segmentation algorithms including an efficient graph-based algorithm and a spectral clustering ensemble-based algorithm.

Shuang Wang

Papers

A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information

A deep learning framework for remote sensing image registration

Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection

Real-time 'Actor-Critic' Tracking

Natural and Remote Sensing Image Segmentation Using Memetic Computing