Applying vision foundation models to remote sensing change detection (CD) has attracted extensive research attention. These studies employ inherent general knowledge from vision foundation models to enhance CD performance. Existing methods explicitly employ the foundation model as a feature extractor while designing additional learnable modules to bridge the task gap. However, these methods substantially increase the computational burden and memory demand in the inference. This paper therefore focuses on addressing the core challenge of effectively leveraging the knowledge from vision foundation models to enhance CD performance while maintaining computational efficiency. Instead of explicitly utilizing the foundation model, we propose Burden-Free Distillation (BFD), an architecture-agnostic foundation model-based distillation framework for efficient CD. BFD transfers the general knowledge from foundation models to task-specific models, thereby eliminating the dependency on foundation models during inference. Specifically, BFD transfers the foundation model knowledge through Dual-temporal Feature Matching module (DFM). This module enables multi-level feature alignment by computing pixel-wise spatial similarity between the foundation models’ general features and the CD models’ task-specific features. Additionally, we leverage patch contrastive distillation, which transfers localized structural patterns to the CD model to further mitigate task discrepancies between foundation models and CD models. We conduct extensive experiments across multiple foundation models and CD architectures, experimental results demonstrate that BFD effectively adapts the knowledge of foundation models to CD tasks without additional computational burden. Compared to other foundation model-based CD methods, BFD reduces the model parameters by 80.3% and improves IoU by 1.78% on the S2Looking dataset. The code is available at https://github.com/Younger-hua/Burden-Free-Distillation.

Multi-modal remote sensing image template matching is a meaningful and crucial topic in remote sensing image processing. However, due to different imaging mechanisms, there are significant nonlinear radiometric variations among multi-modal remote sensing images, increasing the matching challenge and leading to poor matching performances. To tackle this issue, this paper proposes a Fourier Domain Adaptive Network (FDANet) for multi-modal remote sensing image matching. Firstly, FDANet randomly swaps the low-frequency spectrum information between multi-modal images through the Fourier transform to reduce differences among multi-modal images, enhancing network adaptability to different image modalities and improving the multi-modal image matching performance. Secondly, FDANet extracts domain-invariant features from the transformed images through a deep Siamese network. After that, FDANet performs template matching and achieves high-precision multi-modal remote sensing image matching. In addition, we adopt the contrastive learning loss to optimize the FDANet. Extensive experiments on multi-modal remote sensing image matching demonstrate the effectiveness and advantages of the proposed FDANet.

Deep feature learning methods have shown significant advantages over handcrafted feature-based methods in remote sensing image matching and registration. Existing deep learning methods usually introduce complex modules into the deep convolutional network for more robust feature learning. However, they usually require high computation and memory resources for the computing device and have expensive time costs for image registration. As a basic image-processing task, it is crucial to build a lightweight matching network (LM-Net) for fast and accurate image matching and registration. Unfortunately, the image-matching performance will decrease significantly when we directly compress the deep model to a lightweight one. This article proposes an LM-Net based on the knowledge distillation (KD) learning framework for remote sensing image matching and registration. We first build an LM-Net with three convolutional layers. Then, this article proposes an effective KD approach for network optimization, which transfers the effective knowledge from the deep matching network to LM-Net to improve image-matching performances. Specifically, this article considers the useful information in the instance samples and the relation information between samples. It designs the feature and feature relation distillation learning for LM-Net training. Extensive experimental results and analysis have shown the effectiveness and advantages of the proposed LM-Net. LM-Net can reduce the number of parameters and computational complexity of the matching network. Meanwhile, LM-Net can significantly decrease the time cost and achieve results comparable to those of the deep model. It reduces the average image registration time by 42% on remote sensing image matching and registration. Additionally, LM-Net generalizes well on other multimodal remote sensing images.

Image registration and change detection are crucial for multitemporal remote sensing image analysis. The images should be registered before the change information detection. Existing deep learning methods have shown significant advantages in image registration and change detection tasks. They usually design two independent task-specific deep networks for image registration and change detection, respectively. These independent deep networks will learn from scratch and rely on many task-specific labeled training datasets. This article finds that image registration and change detection have similar learning mechanisms, which focus on extracting discriminative features. Inspired by this, we propose a Unified image Registration and Change detection Network (URCNet) that can perform image alignment and change information detection through a single network. Additionally, this article proposes various deep collaborative learning methods for URCNet optimization, which enforce that the URCNet can effectively support remote sensing image registration and change detection simultaneously. Extensive experiments demonstrate the effectiveness of the proposed URCNet for image registration and change detection, which can achieve comparable and better results with task-specific and more complex deep networks. The proposed URCNet can support multitasks based on the same scene images, different scene images, and even multimodal images. Moreover, URCNet shows significant advantages over other deep networks in change detection under limited labeled datasets.

Feature descriptor-based methods have demonstrated remarkable performance in remote sensing image patch matching tasks and are usually optimized using contrastive loss and triplet loss. However, these optimization losses focus on calculating the distance between samples, ignoring the rich information of higher-order feature relationships between multiple image patches. The latter provides valuable information that can be used to improve task performance. Inspired by the superior performance of second-order relations in graph matching and clustering tasks, we aim to exploit the rich information available from high-order relations fully. This paper proposes a high-order relationship (HOR) learning method for remote sensing image patch matching. This method combines low-order feature relations between image patch pairs and high-order feature relations between multiple patches to enhance image matching performance. Extensive experimental results on a multimodel remote sensing image dataset, SEN 1-2, consisting of optical and SAR images, demonstrate that the proposed HOR learning method can improve the performance of remote sensing image patch matching.

Multi-modal images can obtain complementary and rich information images, which are more widely used in various applications. However, due to the different imaging mechanisms of different sensors, there are significant domain distribution differences between multi-modal images. In multi-modal image matching, existing deep learning methods should deal with the image content difference caused by rotation transformation and the domain distribution difference caused by different sensors, which are very difficult for the deep network. To address this issue, we propose to combine an instance comparison and a batch comparison to deal with image content differences and domain distribution differences, respectively. We design a new domain distribution alignment method to explicitly constrain the sample domain distribution of the multi-modal images are consistent through the domain distribution alignment loss. Extensive multi-modal remote sensing image patch matching experiments have shown the effectiveness of the proposed method. Furthermore, the proposed multi-modal domain distribution alignment method has more obvious advantages when there are significant content differences and distribution differences.

Due to the powerful feature extraction capabilities of deep neural networks, traditional approaches are gradually replaced by deep learning approaches for image matching tasks. For multi-modal image patch matching, the deep model should mainly learn the modality-invariant features. For multi-modal images with rotation transformation (RT), the deep model should learn the modality-invariant features and rotation-invariant features simultaneously. However, the performance of the latter trained model is degraded for the former task. The main reason is that the modality invariance of the features degenerates. This paper proposes a deep multi-modal remote sensing image matching network (DCMNet) that combines descriptor learning and continuous learning to solve this problem. Firstly, DCMNet is trained for learning modality-invariant features in multi-modal image patch matching. Then, DCMNet is optimized for multi-modal image patch matching with RT. In the later learning process, we reduce the change of important parameters for the modality-invariant features learning. Experiments demonstrate the effectiveness and robustness of DCMNet in alleviating the modal invariance degradation problem of features.

Depression has been considered the most dominant mental disorder over the past few years. To help clinicians effectively and efficiently estimate the severity scale of depression, various automated systems based on deep learning have been proposed. To estimate the severity of depression, i.e., the depression severity score (Beck Depression Inventory-II), various deep architectures have been designed to perform regression using the Euclidean loss. However, they do not consider the label distribution, and they do not learn the relationships between the facial images and BDI-II scores, which can be resulting in the noisy labeling for automatic depression estimation (ADE). To mitigate this problem, we propose an automated deep architecture, namely the self-adaptation network (SAN), to improve this uncertain labeling for ADE. Specifically, the architecture consists of four modules: (1) ResNet-18 and ResNet-50 are adopted in the deep feature extraction module (DFEM) to extract informative deep features; (2) a self-attention module (SAM) is adopted to learn the weights from the mini-batch; (3) a square ranking regularization module (SRRM) to create high partitions and low partitions is proposed; and (4) a re-label module (RM) is used to re-label the uncertain annotations for ADE in the low partitions. We conduct extensive experiments on depression databases (i.e., AVEC2013 and AVEC2014) and obtain a performance comparable to the performances of other ADE methods in assessing the severity of depression. More importantly, the proposed method can learn valuable depression patterns from facial videos and obtain a performance comparable to the performances of other methods for depression recognition.