Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have become popular in remote sensing (RS) image change detection (CD). However, CNNs often focus on the extraction of spatial information but ignore important spectral and temporal sequences for hyperspectral images (HSIs). In this article, we propose a joint spectral, spatial, and temporal transformer for hyperspectral image change detection (HSI-CD), named SST-Former. First, the SST-Former position-encodes each pixel on the cube to remember the spectral and spatial sequences. Second, a spectral transformer encoder structure is used to extract spectral sequence information. Then, a class token for storing the class information of a single temporal HSI concatenates the output of the spectral transformer encoder. The spatial transformer encoder is used to extract spatial texture information in the next step. Finally, the features of different temporal HSIs are sent as the input of temporal transformer, which is used to extract useful CD features between the current HSI pairs and obtain the binary CD result through multilayer perceptron (MLP). We evaluate the SST-Former on three HSI-CD datasets by numerous experiments, showing that it performs better than other excellent methods both visually and qualitatively. The codes of this work will be available at https://github.com/yanhengwang-heu/IEEE_TGRS_SSTFormer .

Supervised deep learning-based hyperspectral image change detection (HSIs-CD) has demonstrated excellent performance. However, current methods require many labeled training samples, and labeling the dataset is labor-intensive, limiting the application of high-precision supervised learning. Besides, there has been lack of breakthrough in unsupervised HSIs CD methods due to the different feature distributions of bi-temporal HSIs. Here, we propose a semi-supervised domain alignment transformer (DA-Former) for HSIs-CD to address the issues with limited samples. Specifically, a dual-branch transformer autoencoder (TAE) is designed, where the middle layer weights of the dual-branch transformer are shared, pulling features from different data into the same space. Moreover, the TAE is also trained cyclically to align the domains. Although the bi-temporal HSIs features are cross-domain, there is still confusion between the features of different objects. Thus, two fully connected (FC) layers are employed to classify the HSIs middle features extracted by the TAE into changed class or unchanged class with limited labeled data. Three HSIs-CD datasets are used to test this method, showing that TAE can align bi-temporal HSIs domains and achieve the highest accuracy compared with benchmark approaches. The code of the proposed method will be published at https://github.com/yanhengwang-heu/IEEE_TGRS_DA-Former.

Global navigation satellite system (GNSS), inertial measurement unit (IMU), and light detection and ranging (LiDAR), as common sensors in navigation systems, are widely used in fusion simultaneous localization and mapping (SLAM) to improve the performance. However, strong robustness and high efficiency are not received much attention, posing challenges for ubiquitous application of robotics systems. This article presents a robust and fast fusion odometry with inertial navigation system (INS)-centric multiple modalities. Specifically, our framework is based on two subsystems: the GNSS-IMU subsystem estimates coarse states and provides global constraints, the LiDAR-IMU subsystem refines states and constructs the 3-D map. The state estimation is performed by iterated error state Kalman filter without interruption even if partial sensor fails. Moreover, a fast and dynamic frame transformation method and distributed backward propagation are designed for linking world-frame states with global measurements and coping with asynchronism data. Extensive qualitative and quantitative experiments are also conducted on public datasets and in real-world scenarios. The results show that our system exhibits competitive accuracy and efficiency, with strong robustness against sensor degradation and failure.

Hyperspectral image change detection (HSIs-CD) often faces the challenge of limited sample sizes, and labeling data is both time-consuming and labor-intensive. Foundation models leverage extensive unlabeled data for self-supervised generative pre-training, allowing the model to learn rich data representations. However, few models have been specifically designed for HSIs, and existing methods often rely on pre-training datasets that are limited to data from a small number of satellite sensors. This limitation affects generalization, especially when there are significant differences between data from different sensors. Moreover, the difference map (DMP) of bi-temporal HSIs is often used as input to the networks. While the DMP-based approach reduces FLOPs, it may lead to information loss compared to dual-branch networks. In this letter, we propose a mini-patch-based generative pre-trained spectral-spatial transformer (GPSST) for semi-supervised HSIs-CD. We begin by collecting public HSIs datasets and dividing them into thousands of patches. Each patch is then split into spectral-spatial tokens, with a portion of these tokens masked and used as input for the GPSST. We then design a spectral-spatial masked autoencoder as the backbone of GPSST for self-supervised generative learning. Finally, we fine-tune the GPSST encoder using a small number of labeled patches and design a principal component analysis (PCA) branch to compensate for the information loss caused by the DMP. Our experiments demonstrate that GPSST outperforms existing methods, achieving superior accuracy in HSIs-CD.

Automatic open-pit mine extraction and change detection from high-resolution remote sensing images are of great importance to mineral resource management. However, the high spatial heterogeneity and spectral variations of mining areas scenarios makes these tasks challenging. Motivated by the strong correlation between the two tasks and their potential mutual benefits, this paper presents a hybrid convolutional neural network (CNN)-transformer multitask network (CTMNet). Constructed in an Encoder-Decoder manner, CTMNet has two sperate extraction paths (EPs) to localize the regions of interest for bi-temporal images, along with a change detection path (CDP) to identify discrepancies by differentiate the multi-scale feature representations from the EPs. As the basic building block for the EP, a CNN-Transformer hybrid block is designed to enhance the global and local feature representation capacity. To cope with the variations in the bi-temporal images, we propose the feature alignment module for the CDP. A hard sample mining based contrastive constraint loss is proposed to emphasize the contributions of hard samples to the training process. Experimental results on a collected open-pit mine extraction and change detection dataset (OMECSet) and two public datasets reveal the validity of the CTMNet when compared to the state-of-the-art methods. The OMECSet and the code of CTMNet have been made public available at: https://figshare.com/s/80519cb980ca54456447.