Yi Cen

Hyperspectral imagery is valuable for accurate detection of land cover changes within a consistent area across time. However, current training paradigms for hyperspectral change detection are usually in supervised form which are not consistent to the heavy labeling cost reality. Moreover, current deep learning methods face limitations due to insufficient temporal dependencies sharing and inadequate densely band-wise spatial position dependencies. To tackle these challenges, we introduce an innovative semi-supervised training paradigm called Conflict HyperMatch and a deep learning model called Temporal Weights-sharing and Band-wise Spatial Interacting Transformer (TWBSIT) for hyperspectral change detection. The key contributions of this study are as follows: 1) Introduction of the Conflict HyperMatch training schedule, which relies on representation discrepancy and weak-to-strong prediction consistency, to improve the sample leveraging of both labeled and unlabeled data. 2) Weights-sharing Interacting Temporal Attention (WITA) Module is contributed to capture shared temporal interactions and resemblances. 3) Band-wise Cross Spatial Attention (BCSA) module is introduced to achieve a more extensive spatial perception of the specific central image patch. Extensive experiments conducted on three real datasets validate the effectiveness of the proposed TWBSIT model based on Conflict HyperMatch in leveraging both labeled and unlabeled samples for hyperspectral change detection. This method notably decreases the requirement for labeled training samples and surpasses the performance of many existing hyperspectral change detection methods.

Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage.

Autoencoders (AEs) have gained widespread application in the field of hyperspectral anomaly detection, largely due to their notable effectiveness in efficiently reconstructing backgrounds within hyperspectral images (HSIs). However, the absence of prior knowledge and constraints imposed by spectral information capacity hinder the accuracy of anomaly detection by allowing AEs to reconstruct both anomalous targets and backgrounds simultaneously. To address this limitation, a spatial–spectral cross-guided masked autoencoder (SSCMAE) has been proposed. The guided mask is generated based on the spectral difference between the anomaly and the background. This mask effectively suppresses the reconstruction of anomalous targets while enhancing the accuracy of background reconstruction. Moreover, a dual-branch structure operates, encompassing spatial and spectral dimensions, effectively capturing the inherent three-dimensional characteristics present in HSIs. Ingeniously designed cross-connection layers within the architecture enhance the spatial and spectral branches' capability of extracting internal spatial and spectral features of images. In order to capture a more comprehensive range of background features, a lightweight three-dimensional convolutional autoencoder is introduced. This addresses the issue of local feature loss during background reconstruction and overcomes the limitations that visual transformers face when learning local image structures. The proposed method has been systematically compared against several advanced methods on six real-world datasets. The results explicitly demonstrate the efficacy and superior performance of the presented SSCMAE approach.

Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current deep learning-based approaches often neglect critical feature interactions across temporal, spatial, and spectral domains. To address these challenges, we introduce a semi-supervised model called the HyperMatch-based Cross Temporal and Spatial Interaction Transformer (CTSIT) for hyperspectral change detection. The key contributions of this study are as follows: 1) Introduction of the HyperMatch training schedule, which relies on weak-to-strong consistency, to enhance feature extraction using both labeled and unlabeled data. 2) Contribution of the Cross Temporal Bidirectional Attention (CTBA) module to emphasize bidirectional temporal interactions and resemblances. 3) Introduction of the Spatial and Spectral Attention (SSA) module, which includes the Dense Cross Spatial Attention (DCSA) and Spectral Attention (SA) modules, to capture long-range densely spatial interactions and internal spectral similarities. Extensive comparative experiments conducted on three mainstream hyperspectral change detection datasets confirm the effectiveness and superiority of the proposed HyperMatch-based CTSIT in utilizing both labeled and unlabeled samples. For instance, the training ablation demonstrates that this method significantly outperforms most existing state-of-the-art (SOTA) hyperspectral change detection methods, even with a significantly smaller number of samples.

Water reflectance anisotropy has serious implications for the remote sensing of water surfaces. The ${\bm{f}^{\prime}}/{\bm{Q}}$ factor serves as a crucial bridge connecting the inherent and apparent optical properties of turbid water bodies; ${\bm{f}^{\prime}}/{\bm{Q}}$ varies not only with angular geometry and inherent optical properties (IOPs), but also with wavelength. However, there was limited research on ${\bm{f}^{\prime}}/{\bm{Q}}$ in turbid water bodies. This research conducted preliminary exploration on ${\bm{f}^{\prime}}/{\bm{Q}}$ in inland turbid water bodies based on measurements of in-situ multi-angle bidirectionality reflectance, including 17 angles and 251 bands when the solar zenith angle ( ${{\bm{\theta }}}_{\bm{s}}$ ) was between 40° and 50°. This study indicated that ${\bm{f}^{\prime}}/{\bm{Q}}$ exhibited highly sensitivity in the wavelength range of 690–750 nm with a peak near 727 nm, as well as angular geometry played an important role in the BRDF. The ${\bm{f}^{\prime}}/{\bm{Q}}$ varied over the range of 0.13–0.18 sr ⁻¹ , variation of 0.06 sr ⁻¹ across different bands, when viewing zenith angle ( ${{\bm{\theta }}}_{\bm{v}}$ ) and particle backscattering ratio ( ${{\tilde{\bm{b}}}}_{{\bm{bp}}}$ ) were 0 and 0.0183, respectively; higher than the range observed for the ocean (0.08–0.15 sr ⁻¹ ). The variability in ${\bm{f}^{\prime}}/{\bm{Q}}$ as a function of wavelength must be accounted for in turbid waters. The ${\bm{f}^{\prime}}/{\bm{Q}}$ was correlated with the IOPs in the range of 690–750 nm, with coefficient of determination ( R ² ) values higher than 0.94 and root mean square error (RMSE) values lower than 0.004. Our findings are of great significance for understanding the relationship between inherent and apparent optical properties in inland water bodies, for BRDF studies, and for improving the accuracy of satellite product retrieval.