Ship Segmentation in SAR Images by Improved Nonlocal Active Contour Model.

Synthetic aperture radar (SAR) has been widely used in ocean surveillance. As an important part of shipping management and military applications, ship monitoring is a study hotspot in SAR image interpretation; hence, many researches focus on ship targets. Among these studies, ship segmentation is a basic work, but still remains challenging due to the speckle noise and the complicated backscattering phenomenology in SAR images. To solve the problems, this paper proposes a new method for ship segmentation by nonlocal processing. Firstly, the proposed nonlocal energy describes the nonlocal comparison of patches and optimizes regions with spatially-varying features. Secondly, we rewrite the energy functional by introducing a ratio distance defined with respect to the probability density functions of regions to overcome the influence of the multiplicative noise. Finally, the integral histogram is introduced into the pairwise interactions to fasten the speed of convergence. Several rounds of comparative experiments are implemented on real SAR data with different resolutions and bands. The results demonstrate that the proposed method is robust to the speckle noise and intensity variations and could achieve refined segmentation for ship targets.

Locality Preservation for Unsupervised Multimodal Change Detection in Remote Sensing Imagery.

Multimodal change detection (MCD) is a topic of increasing interest in remote sensing. Due to different imaging mechanisms, the multimodal images cannot be directly compared to detect the changes. In this article, we explore the topological structure of multimodal images and construct the links between class relationships (same/different) and change labels (changed/unchanged) of pairwise superpixels, which are imaging modality-invariant. With these links, we formulate the MCD problem within a mathematical framework termed the locality-preserving energy model (LPEM), which is used to maintain the local consistency constraints embedded in the links: the structure consistency based on feature similarity and the label consistency based on spatial continuity. Because the foundation of LPEM, i.e., the links, is intuitively explainable and universal, the proposed method is very robust across different MCD situations. Noteworthy, LPEM is built directly on the label of each superpixel, so it is a paradigm that outputs the change map (CM) directly without the need to generate intermediate difference image (DI) as most previous algorithms have done. Experiments on different real datasets demonstrate the effectiveness of the proposed method. Source code of the proposed method is made available at https://github.com/yulisun/LPEM.

Deep Learning for Visual Speech Analysis: A Survey.

Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation. To push forward future research on visual speech, this paper will present a comprehensive review of recent progress in deep learning methods on visual speech analysis. We cover different aspects of visual speech, including fundamental problems, challenges, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. Besides, we also identify gaps in current research and discuss inspiring future research directions.

Image Regression With Structure Cycle Consistency for Heterogeneous Change Detection.

Change detection (CD) between heterogeneous images is an increasingly interesting topic in remote sensing. The different imaging mechanisms lead to the failure of homogeneous CD methods on heterogeneous images. To address this challenge, we propose a structure cycle consistency-based image regression method, which consists of two components: the exploration of structure representation and the structure-based regression. We first construct a similarity relationship-based graph to capture the structure information of image; here, a k -selection strategy and an adaptive-weighted distance metric are employed to connect each node with its truly similar neighbors. Then, we conduct the structure-based regression with this adaptively learned graph. More specifically, we transform one image to the domain of the other image via the structure cycle consistency, which yields three types of constraints: forward transformation term, cycle transformation term, and sparse regularization term. Noteworthy, it is not a traditional pixel value-based image regression, but an image structure regression, i.e., it requires the transformed image to have the same structure as the original image. Finally, change extraction can be achieved accurately by directly comparing the transformed and original images. Experiments conducted on different real datasets show the excellent performance of the proposed method. The source code of the proposed method will be made available at https://github.com/yulisun/AGSCC.

Deep Ladder-Suppression Network for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific variations undermines the effectiveness of the learned features. To address this problem, we propose a novel, yet elegant module, called the deep ladder-suppression network (DLSN), which is designed to better learn the cross-domain shared content by suppressing domain-specific variations. Our proposed DLSN is an autoencoder with lateral connections from the encoder to the decoder. By this design, the domain-specific details, which are only necessary for reconstructing the unlabeled target data, are directly fed to the decoder to complete the reconstruction task, relieving the pressure of learning domain-specific variations at the later layers of the shared encoder. As a result, DLSN allows the shared encoder to focus on learning cross-domain shared content and ignores the domain-specific variations. Notably, the proposed DLSN can be used as a standard module to be integrated with various existing UDA frameworks to further boost performance. Without whistles and bells, extensive experimental results on four gold-standard domain adaptation datasets, for example: 1) Digits; 2) Office31; 3) Office-Home; and 4) VisDA-C, demonstrate that the proposed DLSN can consistently and significantly improve the performance of various popular UDA frameworks.

Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

This work presents a robust graph mapping approach for the unsupervised heterogeneous change detection problem in remote sensing imagery. To address the challenge that heterogeneous images cannot be directly compared due to different imaging mechanisms, we take advantage of the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. The proposed method first constructs a robust K -nearest neighbor graph to represent the structure of each image, and then compares the graphs within the same image domain by means of graph mapping to calculate the forward and backward difference images, which can avoid the confusion of heterogeneous data. Finally, it detects the changes through a Markovian co-segmentation model that can fuse the forward and backward difference images in the segmentation process, which can be solved by the co-graph cut. Once the changed areas are detected by the Markovian co-segmentation, they will be propagated back into the graph construction process to reduce the influence of changed neighbors. This iterative framework makes the graph more robust and thus improves the final detection performance. Experimental results on different data sets confirm the effectiveness of the proposed method. Source code of the proposed method is made available at https://github.com/yulisun/IRG-McS.

Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation.

Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled source domain with a different but related distribution. Many existing approaches typically learn a domain-invariant representation space by directly matching the marginal distributions of the two domains. However, they ignore exploring the underlying discriminative features of the target data and align the cross-domain discriminative features, which may lead to suboptimal performance. To tackle these two issues simultaneously, this paper presents a Joint Clustering and Discriminative Feature Alignment (JCDFA) approach for UDA, which is capable of naturally unifying the mining of discriminative features and the alignment of class-discriminative features into one single framework. Specifically, in order to mine the intrinsic discriminative information of the unlabeled target data, JCDFA jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data, where the classification of the source domain can guide the clustering learning of the target domain to locate the object category. We then conduct the cross-domain discriminative feature alignment by separately optimizing two new metrics: 1) an extended supervised contrastive learning, i.e., semi-supervised contrastive learning 2) an extended Maximum Mean Discrepancy (MMD), i.e., conditional MMD, explicitly minimizing the intra-class dispersion and maximizing the inter-class compactness. When these two procedures, i.e., discriminative features mining and alignment are integrated into one framework, they tend to benefit from each other to enhance the final performance from a cooperative learning perspective. Experiments are conducted on four real-world benchmarks (e.g., Office-31, ImageCLEF-DA, Office-Home and VisDA-C). All the results demonstrate that our JCDFA can obtain remarkable margins over state-of-the-art domain adaptation methods. Comprehensive ablation studies also verify the importance of each key component of our proposed algorithm and the effectiveness of combining two learning strategies into a framework.