Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance in current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet), for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudo-changes. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet.

Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed.

Cross-spectral local feature matching between visual and thermal images benefits many vision tasks in low-light environments, including image-to-image fusion and camera re-localization. An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of visible–thermal matching is the availability of large-scale and high-quality annotated datasets. However, publicly available datasets are either in relative small quantity scales or have limited pose annotations due to the expensive cost of data acquisition and annotation, which severely hinders the development of this field. In this paper, we proposed a multi-view thermal–visible image dataset for large-scale cross-spectral matching. We first recovered a 3D reference model from a group of collected RGB images, in which a certain image (bridge) shares almost the same pose as the thermal query. We then effectively registered the thermal image to the model based on manually annotating a 2D-2D tie point between the bridge and the thermal. In this way, through simply annotating one same viewpoint image pair, numerous overlapping image pairs between thermal and visible could be available. We also proposed a semi-automatic approach for generating accurate supervision for training multi-view cross-spectral matching. Specifically, our dataset consists of 40,644 cross-modal pairs with well supervision, covering multiple complex scenes. In addition, we also provided the camera metadata, 3D reference model, depth map of the visible images and 6-DoF pose of all images. We extensively evaluated the performance of state-of-the-art algorithms on our dataset and provided a comprehensive analysis of the results. We will publish our dataset and pre-processing code.

Most state-of-the-art object detection methods suffer from poor generalization due to the domain shift between training and testing datasets. To resolve this challenge, unsupervised cross domain object detection is proposed to learn an object detector for an unlabeled target domain by transferring knowledge from an annotated source domain. Promising results have been achieved via Mean Teacher, however, pseudo labeling which is the bottleneck of mutual learning remains to be further explored. In this study, we find that confidence misalignment of the predictions, including category-level overconfidence, instance-level task confidence inconsistency, and image-level confidence misfocusing, leading to the injection of noisy pseudo labels in the training process, will bring suboptimal performance. Considering the above issue, we present a novel general framework termed Multi-Granularity Confidence Alignment Mean Teacher (MGCAMT) for unsupervised cross domain object detection, which alleviates confidence misalignment across category-, instance-, and image-levels simultaneously to refine pseudo labeling for better teacher-student learning. Specifically, to align confidence with accuracy at category level, we propose Classification Confidence Alignment (CCA) to model category uncertainty based on Evidential Deep Learning (EDL) and filter out the category incorrect labels via an uncertainty-aware selection strategy. Furthermore, we design Task Confidence Alignment (TCA) to mitigate the instance-level misalignment between classification and localization by enabling each classification feature to adaptively identify the optimal feature for regression. Finally, we develop imagery Focusing Confidence Alignment (FCA) adopting another way of pseudo label learning, i.e., we use the original outputs from the Mean Teacher network for supervised learning without label assignment to achieve a balanced perception of the image’s spatial layout. When these three procedures are integrated into a single framework, they mutually benefit to improve the final performance from a cooperative learning perspective. Extensive experiments across multiple scenarios demonstrate that our method outperforms large foundational models, and surpasses other state-of-the-art approaches by a large margin.

Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS). ThermalGS employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input. The position, orientation, and scale of Gaussian primitives are guided by the RGB mesh. We introduce feature encoding and embedding networks to integrate semantic and temporal information into the Gaussian primitives, allowing them to capture dynamic thermal radiation characteristics. Moreover, we construct the Thermal Scene Day-and-Night (TSDN) dataset, which includes multi-view, high-resolution aerial RGB reference images and TIR images captured at five different times throughout the day and night, providing a benchmark for dynamic thermal 3D reconstruction tasks. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the TSDN dataset, with an average absolute temperature error of 1 °C and the ability to predict surface temperature variations over time.