The fusion of hyperspectral images (HSI) and light detection and ranging (LiDAR) data reflects strong complementary advantages, significantly enhancing the accuracy and robustness of land cover classification. However, inherent differences in sensor mechanisms and imaging principles lead to substantial heterogeneity in feature representation and scene characterization between these two modalities, posing challenges for effective multisource remote sensing (RS) fusion. To address this issue, a shared-specific graph construction oriented cross-modal fusion network (S2GCCFN) is proposed for HSI and LiDAR data joint classification. Our framework facilitates deep feature interaction between heterogeneous HSI and LiDAR data to achieve modal homogenization, yielding a high-performance joint representation. The core motivation of this study is to capture one assimilation modality (AM) by discovering latent crossmodal mapping strategies from both HSI and LiDAR data simultaneously. Notably, S2GCCFN mitigates modal heterogeneity while enhancing complementary information exchange. S2GCCFN considers one dynamic graph mapping mechanism that adaptively integrates intramodal and crossmodal features comprehensively to align dual RS modalities and promote RS interaction. By establishing shared feature spaces, S2GCCFN enhances intermodal consistency while preserving modality-specific characteristics, ultimately generating highly discriminative AMs for joint classification. Furthermore, S2GCCFN incorporates an AM reconstruction decoder to jointly address modality reconstruction and classification. Experimental results demonstrate that S2GCCFN outperforms state-of-the-art techniques, improving overall accuracy by 2.66% and 1.34% on average on both datasets, respectively.

Combining their complementary properties, using hyperspectral image (HSI) and light detection and ranging (LiDAR) data improves classification performance. Nevertheless, the heterogeneous capturing instruments and distribution characteristics of these two remote sensing (RS) modalities always limit their application scopes in on-ground observation-related domains. This heterogeneity hinders capturing the crossmodal connection for discriminant information extraction and exchange. In this letter, we propose an assimilation modality mapping guided crossmodal fusion network (AM2CFN) for HSI and LiDAR data joint classification. Our motivation is to explore one RS assimilation modality (RSAM) by exploiting one latent crossmodal mapping strategy from HSI and LiDAR data simultaneously to remove the effect of modality heterogeneity and contribute to information exchange. AM2CFN constructs one level-wise assimilating encoder to simulate modality heterogeneity and enhance regional consistency. Modality intrinsic features are captured in this encoder to provide knowledge for modality assimilation. Furthermore, one RSAM balancing HS and LiDAR properties is explored. AM2CFN constructs one RSAM reconstruction decoder for modality reconstruction and classification. Dual constraints based on solid angle and Kullback-Leibler divergence are considered to restrain the information exchange process toward the optimal direction. Experiments show that AM2CFN outperforms several state-of-the-art techniques qualitatively and quantitatively. AM2CFN increases the overall accuracy (OA) by 2.46% and 1.62% on average on the Houston and MUUFL datasets. The codes will be available athttps://github.com/GEOywb/AM2CFN

Hyperspectral images (HSIs) and digital surface models (DSMs) derived from light detection and ranging (LiDAR) data are packed with rich on-ground object characteristics, making their joint classification crucial for accurate remote sensing (RS) identification. However, variations in illumination properties hinder efficient and precise crossmodal information interaction. This phenomenon further exacerbates the difficulty in correctly identifying corresponding spectral curves, ultimately resulting in suboptimal performance. In this paper, a hybrid updating graph motivated illumination-wise property unification network (HugIpuNet) is proposed to solve these challenges. Our core motivation is to unify global illumination descriptions with assistance from elevations for capturing precise multimodal properties from HSIs and DSMs derived from LiDAR data. HS spectral curves and LiDAR elevations interact constantly, considering their geological connection to strengthen their coupling capability. HugIpuNet further considers several illumination-wise mechanisms to address intricate and variable environmental conditions. The whole structure utilizes various hybrid updating graphs to enhance the attribute unification ability in complex environmental scenarios. Dynamic adjacent matrices are constructed to introduce uncertainty to limit the effect caused by description distortions. Experiments show an average 2.86% improvement in overall accuracy (OA) compared with multiple state-of-the-art approaches. The detailed datasets and codes will be available at https://github.com/weixinttt/HugIpuNet.

Purpose: Analyzing the medication pattern of Chinese medicine for asthma based on data mining technology. Method: We searched the relevant prescriptions for the treatment of asthma included in the Cloud Platform of Ancient and Modern Medical Cases since its establishment, screened the literature according to the inclusion and exclusion criteria, and used Microsoft Excel 2010 to carry out the frequency statistics of single medicines and drug combinations, and the Apriori algorithm of IBM SPSS Modeler 18.0 statistical software to analyze the association rules and compounding patterns of Chinese medicines. The high frequency Chinese medicines were analyzed by using IBM SPSS Statistics 26.0 for cluster analysis. Results: A total of 418 prescriptions were included, involving 395 flavors of traditional Chinese medicine (TCM), with a cumulative frequency of 5425 times, among which the top 20 drugs in terms of frequency of use had a cumulative frequency of 2280 times, and those with a frequency of use of ⩾ 115 times were, from highest to lowest, as follows: Licorice (274), Ephedra (200), Almond (179), Banxia (176), Skullcap (117), Tuckahoe (116). The four qi were dominated by warm (35.03%) and cold (34.01%); the five flavors were dominated by pleasant (30.77%) and bitter (29.60%); and the attributed meridians were dominated by the lung meridian (21.43%) and the liver meridian (18.44%).The association analysis yielded six sets of core pairs, of which those with ⩾ 15% support and ⩾ 80% confidence were (Licorice - Almond + Ephedra), (Ephedra - Assarium), (Licorice - Tangerine peel), (Licorice - White peony), ( Licorice - Cinnamon branches), (Ephedra - Assarium + Licorice). Conclusion: The prescription of Chinese medicine for bronchial asthma is based on resolving phlegm and calming asthma, warming the lungs and resolving drinks, clearing the lungs and relieving cough, tonifying the lungs and the spleen, restoring wind and relieving spasm, and dispelling blood stasis and clearing the channels.

Objective: Using data mining technology to explore the medication law of chronic cholecystitis. Methods: We searched the literature of all acupuncture prescriptions for treating menstrual headache since the establishment of the Chinese Journal Full-text Database (CNKI), Wanfang Data Resource System Library (WF), Chinese Biomedical Literature Database (CBM), and Wipo Journal Full-text Database (VIP) databases, extracted the valid prescriptions according to the exclusion criteria, import Microsoft Office Excel 2021 to establish the database of traditional Chinese medicine in the treatment of chronic cholecystitis and used Excel 2021, IBM SPSS Modeler18.0, and IBM SPSS Statistics 27.0 to analyze the database of menstrual acupoint frequency analysis, acupoint attribution analysis, association rule analysis, and cluster analysis. Results: There was a total of 501 prescriptions, designed for 193 flavours of Chinese medicine, with a total application frequency of 5292 times, and the highest frequency of single flavour use was for Radix Bupleuri, Licorice, and Radix Paeoniae Alba, which were mainly used with cold, warm, bitter, and pungent medicines, and there was a total of 10 groups of core pairs of medicines, with 3 classes obtained through clustering.

Fusing hyperspectral (HS) and light detection and ranging (LiDAR) data is capable of enhancing land-cover interpretation capability in multimodal remote sensing (RS) tasks. Nevertheless, nonuniform surrounding illumination in real-world capturing scenarios tends to distort the spectral curves of on-ground objects, inevitably resulting in poor classification performance. This nonuniform illumination further interferes with the cross-modal RS information interaction procedure. In this article, an Illumination Insensitive Modalitywise Assimilation guided Multistage Interaction network (MAMInet II) is proposed to tackle this challenge. Our primary motivation is to eliminate complicated illumination interference (CII) and capture high-level modalitywise assimilation information (MAI) from HS and LiDAR data simultaneously. Notably, the geological connection between these two RS modalities is emphasized in MAMInet II, making the joint classification framework interpretable. Specifically, MAMInet II presents one channelwise illumination removal mechanism by simulating real-world illumination conditions and associating HS spectral curves with LiDAR elevations. One generative switching mechanism contributes to closing the cross-modal RS distribution gap and enhancing the cross-modal capturing ability. These mechanisms are extended to multiscale and multistage versions for aligning local and global RS properties. Experiments illustrate that MAMInet II outperforms several state-of-the-art techniques qualitatively and quantitatively. All source codes are available athttps://github.com/weixinttt/MAMInet-II.