Zili Liu

Keyan Chen, Chengyang Liu, Wenyuan Li et al.

Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatio-temporally complex remote sensing scenarios still presents a substantial challenge. The recent emergence of foundation models, with their powerful universality and generalization capabilities, offers potential solutions. However, bridging the gap of data and tasks remains a significant obstacle. In this paper, we introduce Time Travelling Pixels (TTP), a novel approach that integrates the latent knowledge of the SAM foundation model into change detection. This method effectively addresses the domain shift in general knowledge transfer and the challenge of expressing homogeneous and heterogeneous characteristics of multi-temporal images. The state-of-the-art results obtained on the LEVIR-CD underscore the efficacy of the TTP. The Code is available at \url{https://kychen.me/TTP}.

Zipeng Qi, Chenyang Liu, Zili Liu et al.

Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene. Recent methods leverage 3D information extracted from an Implicit Neural Field (INF), bolstering result consistency across multiple views while using limited accounts of labels (even within 3-5 labels) to streamline labor. Nonetheless, achieving superior performance within the constraints of limited-view labels remains challenging due to inadequate scene-wide supervision and insufficient semantic features within the INF. To address these. we propose to inject the prior of the visual foundation model-Segment Anything(SAM), to the INF to obtain better results under the limited number of training data. Specifically, we contrast SAM features between testing and training views to derive pseudo labels for each testing view, augmenting scene-wide labeling information. Subsequently, we introduce SAM features via a transformer into the INF of the scene, supplementing the semantic information. The experimental results demonstrate that our method outperforms the mainstream method, confirming the efficacy of SAM as a supplement to the INF for this task.

Zili Liu, Hao Chen, Wenyuan Li et al.

Detecting clouds and snow in remote sensing images is an essential preprocessing task for remote sensing imagery. Previous works draw inspiration from semantic segmentation models in computer vision, with most research focusing on improving model architectures to enhance detection performance. However, unlike natural images, the complexity of scenes and the diversity of cloud types in remote sensing images result in many inaccurate labels in cloud and snow detection datasets, introducing unnecessary noises into the training and testing processes. By constructing a new dataset and proposing a novel training strategy with the curriculum learning paradigm, we guide the model in reducing overfitting to noisy labels. Additionally, we design a more appropriate model performance evaluation method, that alleviates the performance assessment bias caused by noisy labels. By conducting experiments on models with UNet and Segformer, we have validated the effectiveness of our proposed method. This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.