Change detection of heterogeneous multitemporal satellite images is an important and challenging topic in remote sensing. Since the imaging mechanisms of heterogeneous sensors are different, it is not possible to directly compare heterogeneous images to detect changes as in the homogeneous images. To address this challenge, we propose an unsupervised image regression-based change detection method based on the structure consistency. The proposed method first adaptively constructs a similarity graph to represent the structure of a pre-event image, then uses the graph to translate the pre-event image to the domain of the post-event image, and then computes the difference image. Finally, a superpixel-based Markovian segmentation model is designed to segment the difference image into changed and unchanged classes. The proposed adaptive structure consistency-based image regression model can not only alleviate the impact of noise and changed pixels on the regression process by using the structure-based transformation, but also easily distinguish between changed and unchanged classes in the difference image by using the prior sparse knowledge of changes. Experimental results on six different datasets demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

Remote sensing image scene classification (RSISC) has broad application prospects, but related challenges still exist and urgently need to be addressed. One of the most important challenges is how to learn a strong discriminative scene representation. Recently, convolutional neural networks (CNNs) have shown great potential in RSISC due to their powerful feature learning ability; however, their performance may be restricted by the complexity of remote sensing images, such as spatial layout, varying scales, complex backgrounds, category diversity, etc. In this paper, we propose an attention-guided multilayer feature aggregation network (AGMFA-Net) that attempts to improve the scene classification performance by effectively aggregating features from different layers. Specifically, to reduce the discrepancies between different layers, we employed the channel–spatial attention on multiple high-level convolutional feature maps to capture more accurately semantic regions that correspond to the content of the given scene. Then, we utilized the learned semantic regions as guidance to aggregate the valuable information from multilayer convolutional features, so as to achieve stronger scene features for classification. Experimental results on three remote sensing scene datasets indicated that our approach achieved competitive classification performance in comparison to the baselines and other state-of-the-art methods.

The circulating tumor DNA (ctDNA), as a promising biomarker of liquid biopsy, has potential clinical relevance on the molecular diagnosis and monitoring of cancer. However, the trace concentration level of ctDNA in the peripheral blood restricts its extensive clinical application. Recently, high-throughput-based methodologies have been leveraged to improve the sensitivity and specificity of ctDNA detection, showing a promising avenue towards liquid biopsy. This review briefly summarizes the high-throughput data features concerned by current ctDNA detection strategies and the technical obstacles, potential solutions, and clinical relevance of current ctDNA profiling technologies. We also highlight future directions improving the limit of detection of ctDNA for better clinical application. This review may serve as a reference for the crosslinks between data science and ctDNA-based liquid biopsy, benefiting clinical translation in advanced cancer diagnosis.

Remote sensing scene classification (RSSC) is a fundamental yet challenging task in the domain of remote sensing (RS). Currently, the methods based on deep features from convolutional neural networks (CNNs) have significantly improved the scene classification accuracy (ACC). However, the standard convolution operations have limited capacity to model the long-range correlations and cannot effectively obtain global contextual understanding ability. In this letter, we propose a novel scene classification framework, termed cross-layer nonlocal network (CL-NL-Net), consisting of a backbone network, a cross-layer nonlocal (CL-NL) module, and a classifier. Among them, the backbone network is used to obtain multilayer convolutional features. The CL-NL module is the core of the proposed method, which captures the long-range correlations between different layers, so as to achieve a better global scene understanding ability. To verify the effectiveness of the proposed CL-NL-Net, we conduct experiments on four benchmark datasets, and the results demonstrate that the proposed method achieves competitive classification ACC and outperforms some state-of-the-art methods.

Heterogeneous change detection (HeCD) is a highly valuable yet challenging task in remote sensing. To enable the comparison of heterogenous images with different imaging mechanisms, some structural consistency-based image transformation methods have been proposed, which utilize graph model to represent image structures and constrain the transformed images and original images to have the same structural characteristics on the graph model. Consequently, these graph based methods face two challenges: adequately characterizing the image structure and effectively utilizing the change information. To address these challenges, this paper proposes a Signed Graph-based Image Transformation method (SGIT) for unsupervised HeCD. Firstly, we analyze the limitations of previous unsigned graph based methods in capturing the image structure, which leads to the failure of detecting changes in some scenes. In light of this, we construct signed graph models that utilize positive/negative weights to represent the similarity/dissimilarity relationships within the image respectively, and employ adaptive weighting, negative sampling and neighborhood expansion strategies to bolster the structure representation capability of signed graphs. Secondly, we analyze how the change would induce a bimodal distribution of vertex feature distances in original and transformed images. Subsequently, a distribution-induced reweighted graph Laplacian regularization (RGLR) is proposed to exploit this prior change information. Finally, an more accuracy image transformation model is obtained by incorporating three types of constraints: signed graph based structural consistency term, bimodal distribution induced RGLR, and change sparsity based penalty term. Extensive comparative experiments on five real datasets have demonstrated the effectiveness of the proposed SGIT.