It is challenging to perform robust road detection on remote sensing images in a complex scene with occlusions by plants and buildings. In this paper, an elaborate dual-decoded U-Net combined with atrous spatial pyramid pooling is proposed to tackle this scenario. In the proposed network, a dual-decoder structure is designed, where a small decoder aims to extract the attention information and it is delivered to the other decoder to enhance the context. Finally, the proposed method is verified on the DeepGlobe dataset. The experiment results demonstrate that the proposed method outperforms other compared methods.

As an important passive location method, pedestrian positioning technology plays a large role in indoor scenes and other scenes that cannot be covered by GNSS signals. To enhance the positioning accuracy of mobile phones, geomagnetic matching and other methods are often used to correct the resulting position. However, some geomagnetic matching methods, such as ICCP, are not accurate enough and require a large amount of calculation. In the meantime, they also lack the heading constraint. When the indoor magnetic interference is severe, it will have a great impact on calculating the magnetic heading and lead to the divergence of the final position. For the above problems, this paper proposes a heading and position constrained algorithm based on indoor geomagnetic characteristics. On the one hand, the QSMF (Quasi Static Magnetic Field) algorithm is used to divide the indoor magnetic quasi-static and non-quasi-static range. In the magnetic instability interval, the non-magnetic heading calculated by the combination of accel and gyro is adopted, and the combination of non-magnetic heading and magnetic heading is adopted in the magnetic stability area, so as to keep it able to continuously provide a stable heading in the case of indoor magnetic interference. On the other hand, in order to reduce the impact of errors caused by low-cost sensor recording onDTW, the inertial recursive information is integrated into geomagnetic DTW, and the combination of inertial information and geomagnetic information is used in calculating the grid module value, so as to improve the final matching success rate and enhance the reliability of final position correction. Experiments show that this method can provide a stable heading in the room with serious magnetic interference, and its heading error accuracy is improved by 70% and position error accuracy is improved by 50%.

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices’ coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder–decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder–decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.

The graph neural network (GNN) has been widely applied to image analysis and recognition. Recently, a semisupervised graph convolutional network (ssGCN) method has been proposed to change detection and obtains promising performance on very-high-resolution remote sensing images. However, a synthetic aperture radar (SAR) image is subject to speckle noise, and there is no explicit structure. In this letter, an end-to-end dynamic graph-level neural network (DGLNN) is proposed to exploit the local structure of each pixel neighborhood block at a graph level and learn a more discriminative graph for change detection. Moreover, in the training of DGLNN, a$K$-nearest neighborhood is employed to reconstruct edges between nodes instead of the fixed edges between two nodes so that each node exploits the features from different neighbor nodes. The proposed method is verified by cross-domain SAR image change detection on four sets of SAR images and compared with five state-of-the-art deep-learning-based SAR image change detection methods. The overall experimental results show that the proposed DGLNN obtains outstanding performance.

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. Although convolutional neural network has been proposed for feature learning, it is necessary to collect numerous of samples to train a perfect model, which is difficult to achieve. In this paper, we propose a few-shot learning-based neural network to exploit the changed information from the noisy difference image. Being different from traditional training method with numerous labeled samples, in the proposed method, fewer samples are used to train a neural network. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.

Graph neural network (GNN) has been widely applied to computer vision as well as remote sensing image analysis. In this paper, we propose an end-to-end graph-level neural network (GLNN) for SAR image change detection. In the proposed method, a GNN is applied to exploit the local structure of an image patch at a graph-level and learn a more discriminative representation. Then, based on these graph representations, change detection is conducted by training an end-to-end neural network. Our method is verified on four cross-dataset of SAR image and compared with three state-of-art deep learning SAR image change detection methods. The experimental results show that the proposed GLNN outperforms other compared methods.

Ensemble learning performs better than a single classifier in most tasks due to the diversity among multiple classifiers. However, the enhancement of the diversity is at the expense of reducing the accuracies of individual classifiers in general and, thus, how to balance the diversity and accuracies is crucial for improving the ensemble performance. In this paper, we propose a new ensemble method which exploits the correlation between individual classifiers and their corresponding weights by constructing a joint optimization model to achieve the tradeoff between the diversity and the accuracy. Specifically, the proposed framework can be modeled as a shallow network and efficiently trained by the end-to-end manner. In the proposed ensemble method, not only can a high total classification performance be achieved by the weighted classifiers but also the individual classifier can be updated based on the error of the optimized weighted classifiers ensemble. Furthermore, the sparsity constraint is imposed on the weight to enforce that partial individual classifiers are selected for final classification. Finally, the experimental results on the UCI datasets demonstrate that the proposed method effectively improves the performance of classification compared with relevant existing ensemble methods.

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most of those networks are too heavy where large memory are necessary for storage and calculation. To reduce the computational and spatial complexity and facilitate the change detection on edge devices, in this paper, we propose a lightweight neural network for bitemporal SAR image change detection. In the proposed network, we replace the regular convolutional layers with bottlenecks, which will not increase the number of channels. Furthermore, we employ dilated convolutional kernels with a few non-zero entries which reduces the FLOPs in convlutional operators. Comparing with traditional neural network, our lightweight neural network will be faster, less FLOPs and parameters. We verify our lightweight neural network on two sets of bitemporal SAR images. The experimental results show that the proposed network can obtain the comparable performance with those heavy-weight neural network.

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a novel mutli-scale average pooling (MSAP) network to exploit the changed information from the noisy difference image. Being different from traditional convolutional network with only an one-scale pooling kernel, in the proposed method, multi -scale pooling kernels are equipped in convolutional network to obtain the spatial context information on changed regions from the difference image. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most methods are developed based on the traditional framework that exploits the changed region from a difference image (DI) that is usually subject to the speckle. To essentially solve this issue, in this paper, we propose a deep canonic correlation network for bitemporal SAR image. In the proposed network, bitemporal SAR images and its corresponding DI are taken as the inputs and then three deep neural networks are designed to employ their features, respectively. Then the changed regions are obtained by the exploited features. Finally, we compare the proposed method with other deep learning methods and perform the comparison on four sets of bitemporal SAR images. The experimental results show that our proposed method outperforms other methods.

The log-ratio (LR) operator has been widely employed to generate the difference image for synthetic aperture radar (SAR) image change detection. However, the difference image generated by this pixelwise operator can be subject to SAR images speckle and unavoidable registration errors between bitemporal SAR images. In this letter, we proposed a spatial metric learning method to obtain a difference image that is more robust to the speckle by learning a metric from a set of constraint pairs. In the proposed method, the spatial context is considered in constructing constraint pairs, each of which consists of patches in the same location of bitemporal SAR images. Then, a semidefinite positive metric matrix M can be obtained by the optimization with the max-margin criterion. Finally, we verify our proposed method on four challenging data sets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-the-art methods.

Most current synthetic aperture radar (SAR) images change detection methods are developed based on a difference image. In this paper, we propose a novel bitemporal polarmetric SAR (PolSAR) images change detection framework based on their land-covers classifications. First, a nearest regularized joint sparse representation (NRJSR) model is developed to exploit the correlations among various polarimetric information and spatial context. Next, a transfer dictionary learning method is proposed for bitemporal PolSAR images classifications. Finally, the changed map can be obtained by comparing these two classification results. The comparison experiment results show that the proposed algorithm obtains better performance.

Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is verified on five sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features benefit change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.

Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. However, most contemporary computer-assisted ASD diagnosis methods ignore the aforementioned age-/sex-related patterns. In this paper, we propose a novel sparse multiview task-centralized (Sparse-MVTC) ensemble classification method for image-based ASD diagnosis. Specifically, with the age and sex information of each subject, we formulate the classification as a multitask learning problem, where each task corresponds to learning upon a specific age/sex group. We also extract multiview features per subject to better reveal the FC changes. Then, in Sparse-MVTC learning, we select a certain central task and treat the rest as auxiliary tasks. By considering both task-task and view-view relationships between the central task and each auxiliary task, we can learn better upon the entire dataset. Finally, by selecting the central task, in turn, we are able to derive multiple classifiers for each task/group. An ensemble strategy is further adopted, such that the final diagnosis can be integrated for each subject. Our comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC ensemble learning can significantly outperform the state-of-the-art classification methods for ASD diagnosis.

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

Sparse representation is an effective model for high-level feature extraction, and the dictionary is critical, since it can provide a sparse and discriminative feature for image classification. However, the traditional sparse model with ℓ1- norm is unstable and ignores spatial context dependence. Furthermore, the traditional off-line dictionary learning is less efficient. In this letter, a high-level feature extraction approach is proposed, in which structured sparsity priors are imposed on the sparse representation to exploit the context dependence and an incremental structured dictionary learning method is proposed to exploit the inherent structures of a dictionary. The experiment results on unsupervised synthetic aperture radar imagery classification show that the structured priors improve classification performance and the proposed algorithm is more efficient in dictionary learning compared with existing works.