Changping Huang

Dependence of spectral upwelling radiance distribution on optical properties and angular geometry in optically complex deep lakes

Yu Zhang, Changping Huang, Lifu Zhang, Danling Tang et autres

The Q -factor is a key parameter characterizing the bidirectional structure of apparent optical properties, yet its variability in optically complex lakes has not been adequately characterized. This study quantitatively assesses and decouples the respective influences of inherent optical properties (IOPs) and …

cn (code pays fourni par la source)

AI for science: Progress, challenges, and perspectives

Yongjun Xu, Zezhi Shao, Xin Liu, Yuquan Li et autres

Modeling Spectral–Temporal Information for Estimating Cotton Verticillium Wilt Severity Using a Transformer-TCN Deep Learning Framework

Yi Gao, Changping Huang, Xia Zhang, Z. C. Zhang

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, …

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Integrating hyper-spectral data and machine learning to monitor cotton Verticillium wilt

Yahui Guo, Yaxin Zhang, Wei Zhou, X. S. Kang et autres

● Identifying the sensitive hyper-spectral bands for cotton VWS ● Evaluating sensitive 2D VI using correlation analysis ● Comparing different machine learning models for detecting VWS ● RF coupled with ratio index outperformed than common models ● Data updating strategy tested the …

cn (code pays fourni par la source)

A Novel Time-Aware Siamese Network for Crop Classification using Spectrotemporal Signatures Derived from Multi-Dimensional Data Format

Arif Ur Rehman, Lifu Zhang, Liang Chen, Changping Huang et autres

Crop classification is critical for precision agriculture, food security and policymaking. While the fusion of multi-sensor data and deep learning holds significant potential for enhancing crop classification, the application of deep learning-based spectral matching techniques in this domain remains underexplored. To address …

Advancing the Earliest Identifiable Timing of Cotton Fields by the Unique Mechanized Plastic-mulching Practices of Xinjiang Cotton

Rubing Zeng, Changping Huang, Junru Zhou, Qiushuang Yao et autres

cn (code pays fourni par la source)

A Novel Time-Aware Siamese Network for Crop Classification Using Spectrotemporal Signatures Derived From Multidimensional Data Format

Arif Ur Rehman, Lifu Zhang, L. Chen, Changping Huang et autres

Crop classification is critical for precision agriculture, food security and policymaking. While the fusion of multi-sensor data and deep learning holds significant potential for enhancing crop classification, the application of deep learning-based spectral matching techniques in this domain remains underexplored. To address …

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Extraction of Habitat Suitability for Fall Armyworm in East Africa Considering Phenological Information

M Wang, Wenjiang Huang, Yingying Dong, Yanru Huang et autres

The fall armyworm (Spodoptera frugiperda), a highly polyphagous and destructive agricultural pest, has posed severe threats to food security in East Africa since its invasion. Identifying habitat suitability for the fall armyworm is critical for early prevention and control. However, existing studies …

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Multiview Prototypical Learning and Semantic Explaining Transformer for Hyperspectral Change Detection

Yixiang Huang, Yi CEN, Ruoxi Song, Wenchao Qi et autres

Abundant hyperspectral remote sensing data provides change detection (CD) technology with the opportunity to accurately distinguish land cover transformations across a consistent area over time. However, most deep learning methods for hyperspectral CD are performed in a fully supervised manner, which requires …

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有限样本稳定分类理论的新疆棉花遥感制图研究

LAN Letao, Changping Huang, ZHOU Junru, Rubing Zeng et autres

Modeling Temporal Resistance Assessment of Cotton to Verticillium Wilt Using Airborne Hyperspectral Data and Disease Progression Rates

Jin Wang, Mi Yang, Zhihong Zheng, Yaohui Gui et autres

Verticillium wilt (VW) is a soil-borne disease that threatens cotton growth and yield. Resistance assessment is crucial for breeding VW-resistant varieties. Drone remote sensing technology has been used to achieve high-throughput resistance evaluation based on late-stage disease severity. However, the timing and …

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Individual Planted Tree Seedling Detection from UAV Multimodal Data with the Alternate Scanning Fusion Method

Taoming Qi, Yaokai Liu, Junxiang Tan, Pengyu Yin et autres

Detection of planted tree seedlings at the individual level is crucial for monitoring forest ecosystems and supporting silvicultural management. The combination of deep learning (DL) object detection algorithms and remote sensing (RS) data from unmanned aerial vehicles (UAVs) offers efficient and cost-effective …

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