Change detection (CD) in image time series (ITS) improves change information quality by modeling change patterns and mitigating seasonal interferences. Recent deep learning (DL)-based methods have made significant progress in this field. However, most of them are based on convolutional neural networks (CNNs) and have limitation in global context extraction. Although newly applied transformer-based methods excel in modeling long-range temporal dependency from ITS, they make insufficient spatial information utilization. Given this, we propose a novel 3-D Swin Transformer with enhanced feature aggregation for ITS CD (TACD), which can comprehensively model long-range spatiotemporal dependencies in ITS. It consists of three main parts: 1) A deformable local context extraction (DLCE) module is integrated in TACD. It embeds ITS into 3-D tokens with rich local contextual information, which helps TACD tickle with multiscale ground changes; 2) A UNet structured 3-D Swin Transformer (3D ST-UNet) is proposed for global modeling. It extracts global spatial-spectral–temporal features from 3-D tokens through the encoder and decoder based on 3-D self-attention and models long-range spatiotemporal dependencies effectively; and 3) An enhanced feature aggregation strategy including the time difference (TD) and self-distillation (SD) modules is designed to promote change modeling. The TD module facilitates feature transmission from the encoder to the decoder by modeling transient changes while the SD module transfers hidden knowledge in the ITS into shallow and deep layers, which capture rich contextual information and enhance feature discrimination. TACD outperforms the state-of-the-art approaches on the OSCD and SpaceNet7 datasets. Experiments on ITS CD and bitemporal CD demonstrate its effectiveness. Our code is available athttps://github.com/silver_m/TACD.

Deep learning-based hyperspectral image (HSI) change detection (CD) approaches have a strong ability to leverage spectral-spatial-temporal information through automatic feature extraction, and currently dominate in the research field. However, their efficiency and universality are limited by the dependency on labeled data. Although the newly applied untrained networks can avoid the need for labeled data, their feature volatility from the simple difference space easily leads to inaccurate CD results. Inspired by the interesting finding that salient changes appear as bright "stripes" in a new feature space, we propose a novel unsupervised CD method that represents and models changes in stripes for HSIs (named as StripeCD), which integrates optimization modeling into an untrained network. The StripeCD method constructs a new feature space that represents change features in stripes and models them in a novel optimization manner. It consists of three main parts: 1) dual-branch untrained convolutional network, which is utilized to extract deep difference features from bitemporal HSIs and combined with a two-stage channel selection strategy to emphasize the important channels that contribute to CD. 2) multiscale forward-backward segmentation framework, which is proposed for salient change representation. It transforms deep difference features into a new feature space by exploiting the structure information of ground objects and associates salient changes with the stripe-shaped change component. 3) stripe-shaped change extraction model, which characterizes the global sparsity and local discontinuity of salient changes. It explores the intrinsic properties of deep difference features and constructs model-based constraints to better identify changed regions in a controllable manner. The proposed StripeCD method outperformed the state-of-the-art unsupervised CD approaches on three widely used datasets. In addition, the proposed StripeCD method indicates the potential for further investigation of untrained networks in facilitating reliable CD.

Deep learning approaches have been extensively applied to change detection in hyperspectral images (HSIs). However, the majority of them encounter scarcity of training samples or rely on complex structures and learning strategies. Although untrained change detection models have been proved to be effective in relief above problems, they were constructed using regular convolutions and treated spatial locations and channels equally, which are insufficient to extract discriminative features and lead to limited accuracy. Given this, a novel untrained framework using randomly initialized models with spatial-channel augmentation (RICD) is proposed for HSI change detection in this paper. It consists of two major modules: 1) an enhanced feature extraction network using successive dilation-deformable feature extraction blocks, which can extract multiscale spatial-spectral features over unfixed sampling locations. It enlarges the field of view of convolutions and takes arbitrary neighborhood into consideration, which helps to increase the discriminativeness of the extracted features; 2) a change sensitive feature augmentation and comparison module integrating feature selection and spatial-channel augmentation strategies, which can exploit spatial context and channel importance. It magnifies difference between changed pixels and unchanged ones and emphasizes contribution of significant channels of the selected change sensitive features. Despite that convolution operations are included in RICD, all the weights are untrained and fixed once they are randomly initialized, indicating that the RICD can work in an unsupervised manner. Its performance is tested over three widely used hyperspectral datasets. Quantitative and qualitative comparisons with several state-of-the-art unsupervised methods reveal the effectiveness of the RICD method.