In this study, we present the element geochemical data of Core XK08-A2 drilled from Lake Xingkai (Khanka), northeast China. The study aims to examine the changes of sediment provenance and ...geochemical composition in response to Asian summer monsoon variability since the last interglacial period. Major elemental analyses on lacustrine samples and sand samples from Lake Xingkai and the surrounding sandy ridges in northeast China indicate that their abundance varied in relatively narrow ranges. The samples had only undergone the primary stage of chemical weathering. Similar Ti/Al and K/Al ratios between the lacustrine samples from Lake Xingkai and the loess-paleosol samples in north China were observed, which suggests that they come from broadly similar desert sources. Due to the significant dependence on grain-size and influence of sediments recycling, Chemical Index of Alteration of the lacustrine sediments could not be regarded as sensitive indicators of source regions' weathering on the glacial-interglacial timescale. Alternatively, the geochemical proxies of the lacustrine sediments could be linked to the Asian summer monsoon through the development of runoff and physical erosion in the catchment. Weakened Asian summer monsoon caused more fine particles transported to the lake basin by reduced runoff in the last glacial period. In contrast, coarser and less-weathered detritus are transported into the lake accompanying strengthened Asian summer monsoon during the warm periods.
•Lacustrine sediments from Lake Xingkai are likely lacustro-aeolian deposits.•Chemical weathering indices are grain-size dependence and influenced by recycling.•Less-weathered detritus are transported into the lake accompanying strengthened runoff transporting capacity.
Hyperspectral image (HSI) classification is one of the hot research topics in the field of remote sensing. The performance of HSI classification greatly depends on the effectiveness of feature ...learning or feature design. Traditional vector-based spectral–spatial features have shown good performance in HSI classification. However, when the number of labeled samples is limited, the performance of these vector-based features is degraded. To fully mine the discriminative features in small-sample case, a novel local matrix feature (LMF) was designed to reflect both the correlation between spectral pixels and the spectral bands in a local spatial neighborhood. In particular, the LMF is a linear combination of a local covariance matrix feature and a local correntropy matrix feature, where the former describes the correlation between spectral pixels and the latter measures the similarity between spectral bands. Based on the constructed LMFs, a simple Log-Euclidean distance-based linear kernel is introduced to measure the similarity between them, and an LMF-based kernel joint sparse representation (LMFKJSR) model is proposed for HSI classification. Due to the superior performance of region covariance and correntropy descriptors, the proposed LMFKJSR shows better results than existing vector-feature-based and matrix-feature-based support vector machine (SVM) and JSR methods on three well-known HSI data sets in the case of limited labeled samples.
This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the ...background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i.e., column-wise). It also assumes the anomalies do not lie in the column subspace of the background and aims to find a randomized subspace of the background to detect the anomalies. First, random techniques including random sampling and random Hadamard projections are implemented to construct a coarse randomized columns subspace of the background with reduced computational cost. Second, anomaly columns are searched and removed from the coarse randomized column subspace by solving a series of least squares problems, resulting in a purified randomized column subspace. Third, the nonzero columns in the anomaly matrix are located by projecting all the pixels on the orthogonal subspace of the purified subspace, and the anomalies are finally detected based on the L2 norm of the columns in the anomaly matrix. The detection performance of RSLAD is compared with four state-of-the-art methods, including global Reed-Xiaoli (GRX), local RX (LRX), collaborative-representation based detector (CRD), and low-rank and sparse matrix decomposition base anomaly detector (LRaSMD). Experimental results show good detection performance of RSLAD with lower computational cost. Therefore, the proposed RSLAD offers an alternative option for hyperspectral anomaly detection.
Montane vegetation belts are sensitive to climate change; however, it is uncertain to what degree their evolution is influenced by changes in mean annual temperature or seasonal climate. In this ...study, we use pollen assemblages from a high elevation lake (3780 m.a.s.l.) in the Gongga Mountains on the southeast margin of the Qinghai-Tibetan Plateau, China, to study changes in altitudinal vegetation zones during the last 12,000 years. The relationships between vegetation belts and winter and summer climate parameters are analyzed. Results indicate that winter temperature mainly controlled the development of evergreen broadleaved forest (Cyclobalanopsis and Taxodiaceae), deciduous broadleaved forest (Betula), and sub-alpine shrubland (Rosaceae, Cyperaceae and Gramineae dominated). In contrast, the development of temperate coniferous forest (Pinus and Tsuga) and alpine herbfield (Artemisia and Chenopodiaceae) was mainly controlled by summer temperature and precipitation. Results show that winter temperature gradually increased from the Greenlandian to Meghalayan, indicating that the main driving factor was winter solar insolation in the Northern Hemisphere. Meanwhile, changes in summer temperature and precipitation are consistent with the results from Indian monsoon-dominated areas of China, suggesting that the summer climate in this region is mainly driven by the migration of the Intertropical Convergence Zone. Our findings suggest that the influence of seasonal climate changes should be considered on the evolution of montane vegetation belts.
•The vegetation dynamics in SW China are reconstructed during the Holocene.•Contrasting effects of seasonal climate on altitudinal vegetation belts evolution.•Winter temperature followed gradual decline with winter insolation in the NH.•Summer climate is mainly driven by migration of the ITCZ under insolation forcing.
Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One ...central issue in this method is the loss of detailed information due to downsampling operations in FCN. To solve this problem, we introduce the maximum fusion strategy that effectively combines semantic information from deep layers and detailed information from shallow layers. Furthermore, this letter develops a powerful backend to enhance the result of FCN by leveraging the digital surface model, which provides height information for VHR images. The proposed semantic segmentation scheme has achieved an overall accuracy of 90.6% on the ISPRS Vaihingen benchmark.
In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive ...models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.
With the development of various deep learning algorithms, the importance and potential of AI + medical treatment are increasingly prominent. Electrocardiogram (ECG) as a common auxiliary diagnostic ...index of heart diseases, has been widely applied in the pre-screening and physical examination of heart diseases due to its low price and non-invasive characteristics. Currently, the multi-lead ECG equipments have been used in the clinic, and some of them have the automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough for the discrimination of abnormal events of ECG, which needs to be further checked by doctors. We therefore develop a deep-learning-based approach for multi-label classification of ECG named Multi-ECGNet, which can effectively identify patients with multiple heart diseases at the same time. The experimental results show that the performance of our methods can get a high score of 0.863 (micro-F1-score) in classifying 55 kinds of arrythmias, which is beyond the level of ordinary human experts.
Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring ...of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively.
Electric vehicle cell industry is an emerging area with fierce competition on technical innovation, in which the patent holder can choose different innovation diffusion options to maximize the ...return; however, the strategy is unclear in certain scenarios. We tried to explain the question of how to maximize the patent holder’s return by appropriate patent license strategy to promote EV cell innovation diffusion, when competition and patent licensing relationship exist in the supply chain. A multistage and multichannel diffusion model of EV cell comprising the patent holder, EV cell producer and EV producers is developed; the evolutionary game is analyzed considering the competition among same stage players and patent licensing relationship among different stage players; and an optimization algorithm is introduced to find the maximum weighted object function of the patent holder. We established the multistage and multichannel diffusion model and found a nonlinear complex relationship between patent holder object function and the key factors including patent royalty pricing and innovation advantage coefficient; in addition, an optimization algorithm is developed based on adopters’ decision-making related with competition and patent licensing.
•A method for extracting coastline information using multi-temporal remote sensing imagery based on tasseled cap transformation and mathematical morphology in a complex marine environment has been ...developed.•The temporal and spatial characteristics of coastline information in the Zhoushan archipelago, China, are analyzed.•Reclamation and construction of seaside projects have changed natural coastlines into man-made coastlines, and the shape of the coastlines has also changed to straight.
The acquisition of dynamic information on the coastline is of great significance for the Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous coastline, numerous tidal flats, and so on have brought many challenges to research involving coastline extraction and the analysis of the spatial–temporal dynamics. This study has developed a method for extracting coastline information using multi-temporal remote sensing images based on tasseled cap transformation and mathematical morphology, allowing the temporal and spatial evolution of the coastline to be analyzed. The results showed that the proposed method can effectively attenuate the influence of suspended sediments, winding coastline, and shoals on the extraction of coastline information, and allow researchers to extract the spatial position of the coastline more accurately. The total coastline length of the Zhoushan archipelago increased by 327.36 km during the study period from 2000 to 2018, the average growth in length was 18.19 km, and the average growth rate was 0.72% over the 19-year period. The total area enclosed by the coastline of the Zhoushan archipelago increased by 112.26 km2 during the study period from 2000 to 2018, the average growth in area was 6.24 km2, and the average growth rate was 0.49% over the 19-year period. Construction, reclamation, and ocean engineering are the main causes of changes to the coastline of the Zhoushan archipelago. This study is useful for accurate coastline information extraction using remote sensing images in a complex marine environment and for protecting coastal resources in the archipelago.
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