Spectral inconsistency is a major obstacle that hinders the widespread application of UV-Visible spectrometry in inland waters. The variance of water spectrum is primarily concentrated in the UV band, leading the existing methods to ignore spectrum features in the visible band and fail to adequately restore subtle spectral details. In addition, existing methods usually rely on a large number of standard samples, making calibration transfer laborintensive and impractical for dynamic field conditions. To address these challenges, we proposed a multi-stage calibration transfer method with minimal standard samples, specifically designed for UV-Visible spectrometry in inland waters. To reduce the number of standard samples and maintain the effectiveness of calibration transfer, Bayesian optimization method was designed to jointly adapt both sample selection and spectral mapping, maximizing the utility of minimal standard samples. Unlike other widely adopted methods that typically require 20 - 60 samples, the proposed approach achieved superior spectral consistency with only 11-13 samples, with mean coefficient of determination (R2) of 0.99 and mean spectral angle of 2.61 degrees. Water quality retrieval models trained on benchtop spectrometers could be directly applied to buoy or handheld spectrometers without additional adjustments, achieving favorable performance (mean R2 = 0.62) comparable to that obtained with benchtop spectrometer (mean R2 = 0.71). To facilitate the field investigation, reference spectrometer can also be switched from benchtop spectrometer to the handheld one. The proposed method significantly improves between-device spectral consistency and facilitates model reuse, which is powerful tool to accelerate application of UV-Visible spectrometry of inland waters, such as buoy-based monitoring and mobile monitoring.

Crop classification is critical for precision agriculture, food security, and policymaking. While the fusion of multisensor data and deep learning holds significant potential for enhancing crop classification, the application of deep learning-based spectral matching techniques (SMTs) in this domain remains underexplored. To address this gap, this study proposes a novel time-aware siamese network (TASN) for spectrotemporal signature (STS) matching, focusing on Kharif crops across three districts in Punjab, Pakistan. The research integrates fusion of Landsat-8/9 and Sentinel-2 data to generate a high-spatial and high-temporal (HSHT) dataset, determines optimal temporal lengths for STS extraction using Jeffries-Matusita (JM) distance analysis, applies smoothing techniques to reduce noise in STS, and evaluates TASN's transfer learning capability for cross-regional crop classification. Results demonstrate that the HSHT dataset significantly enhances temporal resolution, enabling precise crop monitoring. The TASN model achieves 95% accuracy with the universal normalized vegetation index (UNVI), 92% with the normalized difference vegetation index (NDVI), and 91% with the enhanced vegetation index (EVI). Transfer learning experiments reveal robust adaptability; applying STS of one region to others, TASN attains 98% accuracy for rice classification in Hafizabad using UNVI and 81%-87% accuracy for multicrop classification in Lodhran. These findings highlight the efficacy of spectral matching for scalable, transferable crop classification. The study advances agricultural remote sensing by introducing a deep learning framework for STS matching and demonstrating its utility across diverse regions. By combining multisensor fusion and TASN's transferability, this study offers a practical tool for policymakers and farmers, supporting scalable crop monitoring and food security initiatives.

Existing algorithms for chlorophyll-a (Chla) retrieval encounter two key challenges: poor cross-domain generalization, and the scarcity of paired field Chla measurements with corresponding spectrum. Water optical properties vary strongly with environmental conditions, causing pronounced distribution shift across different spatial and temporal domains. However, most models assume that spectra in new domains follow the same distribution as the training data, which leads to sharp performance degradation under distribution shifts. Furthermore, the absence of effective sample selection strategy increases data demand for model development, exacerbating conflicts with limited field observations. To address these issues, we proposed FSResTL-Chla, a fewshot ResNet transfer learning method for Chla retrieval. We constructed a universal Chla-spectrum model with a pre-trained ResNet under different optical water types. To ensure the representativeness and informativeness of few-shot samples, a self-adaptive representative sample selection strategy was proposed, coupled with finetuning to adjust the universal model to distribution shift. FSResTL-Chla showed favorable performance across multiple optical water types using 7-12 samples, with an average coefficient of determination (R2) of 0.68. Despite using only 18% of all observations, FSResTL-Chla achieved performance (R2 = 0.52-0.79) comparable to or exceeding that of the best traditional models (R2 = 0.49-0.80), which commonly required 70% - 80% of the observations for model development. This demonstrates that FSResTL-Chla maintains strong spatiotempral generalization, whereas traditional models often fail under changing conditions. By improving cross-domain generalization while reducing dependence on sample size, FSResTL-Chla provides a valuable tool for environmental monitoring and management.

Long term remote sensing data contains information from four dimensions:time,space,and spectrum.Currently,the description is only based on spatial and spectral dimensions,and there is no unified concept to describe long-term remote sensing data.In traditional research,using a three-dimensional cube model to store long-term remote sensing data would separately store data from different time periods,which cannot meet the efficient storage,fast retrieval,and deep analysis of its time dimension,and thus cannot fully explore the characteristics of different land features changing over time.Essentially,it is still discrete three-dimensional data,and the four-dimensional information of long-term data has not been effectively managed and analyzed uniformly.Therefore,based on the research foundation of predecessors,the concept of time spectrum has emerged,bringing new breakthroughs to the organization and storage of remote sensing data,and improving the utilization value of long-term remote sensing data.Similar to the concept of spectra,remote sensing feature sequences at different times constitute spectrograms. Remote sensing spectrogram theory mainly describes the information of remote sensing data in spectral,temporal,and spatial dimensions.By analyzing the temporal spectrum of remote sensing images,the changing characteristics of surface objects at different time scales and spectral ranges can be revealed,which can be used in various application fields.Experts and scholars in the remote sensing field have fully recognized the scientific value and broad application prospects of MDD. Land change detection and fine classification of crops require the use of multiple spectral information from different time periods.Unlike traditional 3D datasets,the study of remote sensing data has been elevated from 3D to 4D,providing a different way for land features to be represented.This helps to solve the problem of"same spectrum foreign objects,same object different spectrum".The analysis of cultivated land types based on spatiotemporal spectra also has the characteristics of complete phenology.It can effectively eliminate false changes caused by seasonal factors.The MDD format has significantly improved the efficiency and accuracy of remote sensing analysis,and has been widely promoted and applied in multiple industry user units,generating good social and economic benefits. This article summarizes and generalizes the basic concepts,main methods,and techniques of remote sensing spectrogram theory,introduces the application fields and latest research progress of remote sensing spectrogram theory,and finally looks forward to the future research directions of spectrogram theory.The future research directions in this field include the study of spatiotemporal consistency and data fusion of multi-source data,the research of deep learning time-frequency analysis methods,and the research of real-time and high-resolution analysis. In the field of next-generation remote sensing data storage,the multidimensional data format developed by Chinese scientists is expected to meet the needs of real-time online data processing,be used to develop a new spatiotemporal spectral integrated data organization and storage mode,and serve as one of the technical foundations of remote sensing cloud platforms.This innovation can improve the storage,retrieval,and sharing efficiency of remote sensing big data,providing key support for China to build an independent and controllable remote sensing cloud platform.

Monitoring of non-point source pollution (NPSP) in small watersheds suffers from low monitoring frequency and sparse spatial coverage, limiting both our understanding and effective management of NPSP. UV-Visible (UV-Vis) spectrometry, known for its rapid response and cost-effectiveness, offers a promising solution to address this monitoring scarcity. However, key water quality parameters of NPSP, total phosphorus (TP) and total nitrogen (TN) (e.g., ammonia), are inherently challenging to retrieve from UV-Vis spectrum, because TP is optically-nonactive parameter and TN contains optically-nonactive parameter (e.g., ammonia). Moreover, uncertainty evaluation of retrieved water quality relies heavily on concurrent field measurements, lacking effective method for real-time uncertainty assessment. To address these challenges, we constructed a UV-Vis spectrometer network comprising 20 spectrometers and implemented a four-month in-situ NPSP monitoring in Pizhou City, China. Multivariate mixture density network (multi-MDN) was developed to model the covariation relationship among NPSP-related water quality conditioned on the given spectrum, enabling the joint retrieval of TN, TP, and CODMn (chemical oxygen demand). By leveraging this covariation relationship among NPSP-related water quality, the proposed multi-MDN effectively improved generalization capability for both optically-active and optically-nonactive constituents, with R2 of 0.92, 0.84 and 0.90 for TN, TP and CODMn. We further proposed per-estimation uncertainty evaluation method for real-time assessment of NPSP monitoring and designed several experiments to examine its utility. Per-estimation uncertainty effectively identified large retrieval error, detected spectrometer anomalies (e.g., insufficient probe submergence, biofouling), and implied model update. Using the real-time in-situ observation from UV-Visible spectrometer network, we revealed the spatiotemporal variation of NPSP in rural and urban rivers of Pizhou. This study provides an accurate, robust and cost-effective solution to improve spatiotemporal coverage of NPSP monitoring, supporting informed management in small watersheds.

Spectral super-resolution (SSR) has garnered significant attention in recent years. Most existing networks rely on supervised methods, which require paired RGB and hyperspectral images (HSIs) for training. However, HSI acquisition is costly and time-consuming due to specialized hardware and complex preprocessing. In addition, spectral mixing phenomena in low-resolution HSIs degrade image quality. To address these challenges, spectral super-resolution (SSR) techniques have emerged to generate high-quality HSIs from widely accessible RGB images, enabling applications in agriculture, medicine, and environmental monitoring. To address these issues, we propose a novel unsupervised SSR network guided by spectral sampling priors (SPointNet). Inspired by multimodality text-image fusion techniques, we first introduce the point-image fusion module (PI-Fusion), which fuses sampled spectral data with RGB images. We then utilize spectral unmixing for super-resolution module to produce a coarse HSI, maximizing the exploitation of spectral information. Finally, we integrate a multistage shuffle-unshuffle transformer) to fuse the coarse HSI with the RGB image, enhancing its spatial information. SPointNet can ensure continuity and consistency in both spectral and spatial dimensions in the generation of the refined HSI, which is validated on three publicly available datasets.

This study evaluates the effectiveness of hyperspectral data to retrieve chlorophyll a (Chl-a) concentrations using various Machine Learning (ML) methods, specifically to determine whether spectral reflectance can provide accurate estimations of Chl-a. The study aims to address the gap in understanding how hyperspectral measurements correlate with Chl-a concentrations and to explore the potential for improving water quality assessment by accurately estimating Chl-a concentrations, which is essential for environmental monitoring, especially in aquatic ecosystems. The method proposed is evaluated using different Chl-a concentrations defined by the experiment design using Rhodamine B. The main reason for preparing pre-defined solutions of Chl-a is to verify the sensitivity of spectral measurements to Chl-a concentrations. In this paper, we aim to measure the pure signature of the Chl-a in which spectral reflectance of each Chl-a concentration is measured with 10 replicates by the spectrometer HS-1000WFL3. Six ML methods were investigated; (i) the multilayer perceptron artificial neural network (MLPNN), (ii) the support vector regression (SVR), (iii) the random forest regression (RFR), (iv) the Gaussian process regression (GPR), (v) Relevance Vector Machine (RVM) and (vi) Extreme Gradient Boosting (XGboost). 70

Lubricating oil reflects mechanical component aging and wear. Accurate quantification of its wear metals is essential for equipment safety and intelligent maintenance. This study introduces a rapid, non-destructive method for detecting wear metal content in lubricating oil using hyperspectral technology to overcome limitations such as bulky, expensive instruments and destructive testing in current spectroscopic techniques. Absorption spectra of 98 marine gearbox oil samples were acquired using Hach UV-Vis and GLT optical fiber spectrometers. We propose a multi-head attention mechanism enhanced genetic algorithm (MHA-GA) for deep feature extraction, integrating attention weights into band selection and fitness evaluation to identify key features under multi-element interference. Wear metal prediction models were constructed using random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results demonstrate MHA-GA outperformed traditional genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) in feature selection. The MHA-GA-XGBoost model performed best. Fe prediction R2 reached 0.96 (Hach) and 0.93 (GLT), with RPDs of 5.33 and 3.90. For Cu, R2 reached 0.91 and 0.83, with RPDs of 3.35 and 2.42. The results indicate that hyperspectral technology combined with machine learning enables effective non-destructive wear metal quantification, offering a promising strategy for intelligent maintenance and condition monitoring of lubricating oil.

Abstract Background Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied. Methods This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ($$D_{{{\text{HbO}}_{2} }}$$ D HbO 2 ), deoxyhemoglobin ($$D_{{{\text{Hb}}}}$$ D Hb ), total hemoglobin ($$t{\text{Hb}}$$ t Hb ) and tissue water ($$D_{{{\text{water}}}}$$ D water ), and two normalized difference indices of oxyhemoglobin($$ND{\text{HbO}}_{2} I$$ N D HbO 2 I ) and deoxyhemoglobin ($$ND{\text{Hb}}I$$ N D Hb I ) from continuum-removal spectra (400–1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis. Typical statistical analyses were conducted to explore the effectiveness of the proposed HSIs. Results $$D_{{{\text{Hb}}}}$$ D Hb and $$t{\text{Hb}}$$ t Hb exerted significant increasing trends as inflammation progressed, whereas $$D_{{{\text{HbO}}_{2} }}$$ D HbO 2 exhibited significant difference (P < 0.05) from the healthy sites only at moderate and severe periodontitis and $$D_{{{\text{water}}}}$$ D water presented unstable sensitives to disease severity. By contrast, $$ND{\text{HbO}}_{2} I$$ N D HbO 2 I and $$ND{\text{Hb}}I$$ N D Hb I showed more steadily downward trends as severity increased, and demonstrated the highest correlations with clinical gold standard parameters. Particularly, the proposed normalized HSIs ($$ND{\text{HbO}}_{2} I$$ N D HbO 2 I and $$ND{\text{Hb}}I$$ N D Hb I ) yielded high correlations of − 0.49 and − 0.44 with probing depth, respectively, far outperforming results achieved by previous studies. The performances of the HSIs were also confirmed using the periodontal therapy group. Conclusions These results indicated great potentials of combination optical spectroscopy and smart devices to non-invasively probe periodontitis at earlier stages using the simple and practical HSIs. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry on October 24, 2021, and the clinical registration number is ChiCTR2100052306

The content of ancient calligraphy artifacts contains crucial information documenting the social civilization of ancient China.Obtaining and identifying hidden inscriptions within the paper surface is important for historical background research on cultural relics and the collection of humanistic classics.However,traditional methods for identifying hidden inscriptions in calligraphy artifacts rely heavily on manual interpretation,which requires extensive expertise from researchers and results in a time-consuming analysis that may inadvertently cause secondary damage to the artifacts.To address this challenge,hyperspectral remote sensing,with its non-contact and efficient characteristics,captures the spatial properties of paper-based artifacts and acquires rich spectral information.This enables the digitization and storage of calligraphy artifacts.Initially,the Minimum Noise Fraction(MNF)transformation technique was utilized to reveal latent blurry information in calligraphy artifacts.Subsequently,a spectral transformation method based on Linear Difference Enhancement(LDE)was developed to identify these details further,and statistical analysis was conducted on the spectral parameters before and after transformation and the component images extracted by MNF.By utilizing entropy evaluation,we obtained the most information-rich spectral image,ultimately enabling the successful identification of the hidden inscriptions within the calligraphy artifact.The research results demonstrate the following:(1)The MNF transformation of the hyperspectral image of the calligraphy artifact reveals the blurred patterns hidden in the inscriptions.These patterns exhibit similarities in spectral morphology with other content elements of the calligraphy artifact but with differences in reflectivity.(2)The LDE algorithm effectively amplifies the relative differences between hidden information within the calligraphy artifact and the spectral bands of the inscriptions.LDE significantly enhances most spectral features of the hidden inscriptions after enhancement.(3)Following LDE processing,the calligraphy artifact data shows improved image entropy values in the wavelength range,spectral characteristics,and MNF sub-components.Particularly,the entropy value of the spectral variance(SV)image after LDE processing reaches 6.74 bits.(4)After LDE processing,the SV image of the calligraphy artifact successfully identifies the hidden inscriptions on the paper that do not belong to the"Heart Sutra"content.This finding proves that the paper used for this calligraphy artifact is dedicated to sutra writing.This discovery effectively reveals and identifies hidden inscriptions behind Emperor Qianlong's ink treasure,enriching the historical and humanistic background of the artifact.It also provides scientific,theoretical,and technical support for future studies on extracting hidden inscription information in ancient calligraphy artifacts.

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects.

Water environment health assessment is one of the vital fields closely related to the quality of human life. The change of material contained in water will lead to the reflectance change of hyperspectral remote sensing data. According to this phenomenon, the water quality parameters are calculated to achieve the purpose of water quality monitoring. Series knowledge graphs in this field are drawn after analyzing 564 publications from WOS (Web of Science) and EI (The Engineering Index) databases since 1994 with the support of VOSviewer and CiteSpace. Including statistics of documents publication time, contribution analysis, the influence of publications and journals, and the influence of funding institutions. It is concluded that the research trend of hyperspectral water quality monitoring is the machine learning algorithm based on UAV (Unmanned Aerial Vehicle) hyperspectral instrument data by analyzing scientific research cooperation, keyword analysis, and research hotspots. The whole picture of the research is obtained in this field from four subfields: application scenarios, data sources, water quality parameters, and monitoring algorithms in this paper. It is summarized that the miniaturization, integration, and intelligence of hyperspectral sensors will be the research trend in the next 10 years or even longer. The conclusions have significant reference values for this field.

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.

Viscosity is an important index reflecting paper cellulose's degree of polymerization and physical properties. Accurate real time information on viscosity is important for repairing and protecting precious paper materials. However, the traditional paper's viscosity analysis method mainly uses chemical means, which takes a long time and will inevitably cause secondary damage to the paper. To solve this problem, hyperspectral remote sensing, with its rich information and real-time, contactless characteristics, is an effective way to obtain the paper's viscosity content without damage. First, obtain experimental papers with different aging degrees in the laboratory to measure their viscosity contents, collect hyperspectral data of paper samples, preprocess paper samples hyperspectral data through spectral noise reduction, spectral transformation, and spectral information expansion, establish a spectral database of paper's viscosity contents under different aging degrees, and respectively build spectral difference index, ratio index and normalization index under different spectral transformation methods. Correlation analyses were carried out the 12 best spectral indices with the strongest correlation with viscosity were selected. Finally, the selected spectral indices were used as independent variables to build a regression model on the viscosity content. We also selected the spectral index and model that best characterized the change of the paper's viscosity content by comparing model accuracy. The results show that: (1) Compared with the original spectrum, the proportion of the highly correlated feature subset of viscosity extracted after spectral transformation processing greatly improved along with the mean and median of the correlation coefficient; (2) The correlation between the spectral information parameters (obtained by spectral information expansion) and viscosity is higher than that of the original spectral segment, and most of the 12 optimal spectral indices extracted have the participation in the expanded information parameters; (3) The correlation between the best spectral index, extracted under different spectral transformation results, and the viscosity content above 0. 89, and the three representative spectral indices selected from them effectively reflected the change of paper's viscosity at 400 similar to 500 mL center dot g(-1), (4) After logarithmic first-order differential treatment of the paper spectrum, the normalized index constructed by spectral integration and spectral absorption depth has the largest correlation with viscosity, reaching 0. 917 and the R-2 of the model established by this index in the training set and the test set is 0. 84 and 0. 76 respectively, with MRE and RMSE in the test set as 0. 089 and 40. 29 mL center dot g(-1), respectively. The research results can provide scientific theory and technical support for the remote sensing inversion of the paper's viscosity content, and have important reference significance for the construction of the non-destructive analysis system of paper cultural relics.

Maintaining the balance between power station operation and environmental carrying capacity in the process of cooling water discharge into coastal waters is an essential issue to be considered. Earth observations with airborne and sea surface sensors can efficiently estimate distribution characteristics of extensive sea surface temperature compared with traditional numerical and physical simulations. Data acquisition timing windows for those sensors are designed according to tidal data. The airborne thermal infrared data (Thermal Airborne Spectrographic Imager, TASI) is preprocessed by algorithms of atmospheric correction, geometric correction, strip brightness gradient removal, and noise reduction, and then the seawater temperature is inversed in association with sea surface synchronous temperature measurement data (Sea-Bird Electronics, SBE). Verification analyses suggested a satisfied accuracy of less than about 0.2 °C error between the predicted and the measured values in general. Multiple factors influence seawater temperature, i.e., meteorology, ocean current, runoff, water depth, seawater convection, and eddy current; tidal activity is not the only one. Environmental background temperature in different seasons is the governing factor affecting the diffusion effect of seawater temperature drainage according to analyses of the covariances and correlation coefficients of eight tidal states. The present study presents an efficient and quick seawater temperature monitoring technique owing to industrial warm drainage to sea by means of a complete set of seawater temperature inversion algorithms with multi-source thermal infrared hyperspectral data.

Periodontitis is an infectious, destructive and inflammatory disease of periodontal tissue around teeth. The main clinical manifestations of periodontitis are soft tissue pocket formation, clinical attachment loss and alveolar bone resorption. Visible near-infrared spectroscopy, characterized by noninvasive and rapid detection, has been widely used in medicine. The present study explored the use of visible near-infrared spectroscopy in evaluating the relative contents of oxygenated hemoglobin and deoxyhemoglobin in gingival tissue of severe periodontitis. The gingival tissue spectra (400 similar to 1 700 nm) were obtained and processed from 20 sites of 5 healthy subjects and 20 sites of 5 patients with severe periodontitis. Spectra were collected at the gingival margin, 4 and 7 mm below the gingival margin. Our research found that oxygenated hemoglobin and deoxyhemoglobin showed obvious spectral absorption characteristics at 544 and 576 nm respectively. The relative absorption depths of oxygenated hemoglobin and deoxyhemoglobin were calculated from the continuum removal based on the original spectral data. The results showed that the relative contents of oxygenated hemoglobin and deoxyhemoglobin in the periodontal pocket of severe periodontitis were significantly higher than those in the healthy group (p<0. 05). At the same time, there was no significant difference in the contents of oxygenated hemoglobin and deoxyhemoglobin at different depths in the deep periodontal pocket of severe periodontitis. The results reflected the hemodynamic differences between severe periodontitis and healthy gingival tissue, and provided a scientific basis for applying visible near-infrared spectroscopy in noninvasive detection and auxiliary diagnosis of periodontitis.

遥感卫星可快速、动态地获取地震灾区大范围的高分辨率影像,已成为快速获取震后灾情信息的主要技术手段之一.基于震后灾情调查中广泛使用的光学遥感数据和变化检测算法,首先对遥感数据及其产品进行了归纳总结,在此基础上综述了基于高分辨率遥感影像的变化检测算法在震害提取中的应用,阐述了基于像元和面向对象两类变化检测方法的基本原理和优缺点,讨论和总结了应用中存在的问题和不足,以期为未来地震应急中的灾情调查工作提供参考.

The fusion methods are divided into three categories: fusion methods emphasizing on spatial resolution enhancement, fusion methods emphasizing on spectral resolution enhancement and fusion methods emphasizing on temporal resolution enhancement. The rapid development of remote sensing applications has been promoting the resolution of satellite sensors, especially for hyperspectral sensors. This chapter proposes a method of data spectral enhancement method frame utilizing the technique of convolution neural networks to enhance the spectral resolution of multispectral data to acquire adequate hyperspectral data. In remote sensing fusion method, there are many deep-learning-based methods proposed. Zhang Lifu et al. developed a multi-dimensional data format, MDD (Multi-Dimensional Dataset) data format, based on data characteristics of multi-temporal phase, multi-space, and multi-spectral data. According to the different storage order of multidimensional data, MDD can be divided into five data storage formats: temporal sequential in band, temporal sequential in pixel, temporal interleaved by band, temporal interleaved by pixel, and temporal interleaved by spectrum.

Traditional spatiotemporal fusion methods utilize remote sensing images from two or three adjacent dates to predict missing images. Thus, temporal information in longtime data sets is not fully utilized, increasing the costs of long time series construction in terms of time and labor. Here, we propose a synchronous long time-series completion method using a 3-D fully convolutional neural network (LTSC3D). This method can be used to convert long time-series high-temporal-low-spatial (HTLS)- and high-spatial-low-temporal (HSLT)-resolution remotely sensed images into 4-D data sets. From these data sets, we can then extract and fuse spatiotemporal features to produce synchronous long time-series high-temporal-high-spatial (HTHS)-resolution predictions. The model was tested using simulated and real data sets and compared with four representative traditional spatiotemporal fusion methods. The results demonstrated the high accuracy and high efficiency of our method.