The Coastal Intelligent Remote Sensing and Modelling (CIRSM) Team was established in 2016, led by Professor Sun Weiwei from Ningbo University. Comprising 13 faculty members and 37 doctoral and master's degree candidates, it forms a vigorous, stable, and united research team with a strong foundation and a promising future. Additionally, CIRSM is a novel interdisciplinary and cross-institutional research group, fostering collaboration across various academic disciplines and institutions.
Composition of the Faculty Team: The team comprises nine faculty members from Ningbo University and four external professors. The Ningbo University faculty includes Professor Sun Weiwei, Associate Professor Yang Gang, Associate Professor Meng Xiangchao, Associate Professor Feng Tian, Associate Researcher Wang Lihua, Dr. Liu Weiwei, Dr. Wang Yumiao, Dr. Chen Benjie, and Dr. Zhao Rui. The four external professors are Professor Peng Jiangtao from Hubei University, Professor Lv Zhiyong from Xi'an University of Technology, Associate Professor Chen Chao from Suzhou University of Science and Technology, and Associate Professor Fu Bolin from Guilin University of Technology.
Main Research Directions: The team focuses on hyperspectral remote sensing of coastal zones, multi-source remote sensing collaborative processing, long-term remote sensing analysis, and numerical modeling.
Achievements and Honors: The team has achieved a series of high-quality research outcomes in areas such as hyperspectral remote sensing feature mining and target extraction, high spatial and hyperspectral remote sensing fusion, coastal wetland mapping using domestic satellites, and long-term sequence information reconstruction. It has undertaken 32 national, provincial, and ministerial research projects, published 2 academic monographs/textbooks, and authored over 200 SCI journal articles. Additionally, the team has applied for and been granted 13 invention patents, and has received the Second Prize of Surveying and Mapping Science and Technology Award in China, with Professor Sun Weiwei ranking first.
Introduction to Team Equipment:
Unmanned Aerial Vehicle (UAV) remote sensing technology enables swift acquisition of spatial remote sensing information, encompassing geographic data, resources, and environmental information. It accomplishes the comprehensive process of remote sensing data collection, processing, and application analysis, characterized by its flexibility, safety, and economic efficiency. Our team's UAV-mounted remote sensing equipment primarily comprises LiDAR (Light Detection and Ranging), hyperspectral imagers, and multispectral imagers. These instruments leverage distinct physical principles for data collection:
LiDAR employs laser beams to measure distances;
Hyperspectral imagers identify different materials by analyzing the spectral characteristics reflected by the Earth's surface;
Multispectral imagers capture images across multiple discrete wavelength bands to obtain information specific to those bands.
LiDAR:
Instrument Parameters:
Single flight coverage: 2 square kilometers
Scanning distance: 450 meters (at 80% reflectivity)
Elevation accuracy: 5 centimeters
Supports up to 3 echoes
Instrument Features:
High frequency and excellent collimation accuracy
High integration for ease of operation
Independent of external lighting conditions or the target's own radiation, enabling 24/7 all-weather operation
Hyperspectral Imager:
Spectral Range: 400-1000nm; Number of Spectral Channels: 340
Instrument Features:
High spectral resolution, capable of acquiring multiple continuous narrow-band spectral information
Extensive spectral range of the hyperspectral system, covering visible, near-infrared, and shortwave infrared bands
Efficient push-broom imaging from an unmanned aerial vehicle platform, ensuring high operational efficiency
Multispectral Imager:
Image Sensors: Comprising 6 x 1/2.9-inch CMOS sensors, including 1 color sensor for visible light imaging and 5 monochrome sensors for multispectral imaging. Each individual sensor boasts: effective pixels: 2.08 million
Filters: Blue (B): 450 nm ± 16 nm; Green (G): 560 nm ± 16 nm; Red (R): 650 nm ± 16 nm; Red Edge (RE): 730 nm ± 16 nm; Near-Infrared (NIR): 840 nm ± 26 nm
Instrument Features:
Capable of detecting information beyond the visible light spectrum
Generates vegetation index maps
Captures varying degrees of solar irradiance at different times of the day
Acquires precise positional data based on both RGB and multispectral imagery
Outstanding Thesis by the Team:
[1]Peng J, Huang Y, Sun W, et al. Domain adaptation in remote sensing image classification: A survey[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9842-9859.
[2]He K, Sun W, Yang G, et al. A dual global–local attention network for hyperspectral band selection[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-13.
[3]Zhou J, Sun W, Meng X, et al. Generalized linear spectral mixing model for spatial–temporal–spectral fusion[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.
[4]Wang L, Ma H, Li J, et al. An automated extraction of small-and middle-sized rice fields under complex terrain based on SAR time series: A case study of Chongqing[J].Computers and Electronics in Agriculture, 2022, 200: 107232.
[5]Meng X, Yang G, Shao F, et al. SARF: A simple, adjustable, and robust fusion method[J].IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[6]Meng X, Bao K, Shu J, et al. A blind full-resolution quality evaluation method for pansharpening[J].IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-16.
[7]Yang G, Huang K, Sun W, et al. Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 189: 236-254.
[8]Zhang Q, Liu L, Yang G, et al. Effects of wintertime haze on regional thermal environment and urban heat island in the Yangtze River Delta, China[J].Urban Climate, 2023, 47: 101354.
Academic Exchanges and Collaborations:
Our team has hosted numerous academic forums and presented reports at professional conferences in remote sensing, surveying and mapping. We focus on scientific issues within the realm of remote sensing science and technology, advocating for fundamental research, valuing scientific practice, attending to application needs, and exploring frontier fields. Through keynote speeches, expert commentary, interactive Q&A sessions, and other formats, we aim to uncover and showcase the latest research advancements and innovative ideas in remote sensing and related disciplines.
The "6th National Symposium on Imaging Spectroscopy for Earth Observation" (the 12th Symposium on Imaging Spectroscopy Technology and Applications as well as the Interdisciplinary Forum), themed "Imaging Spectroscopy for Earth Observation and Ecological Civilization Construction," was held in Ningbo City, Zhejiang Province from October 14th to 16th, 2021. Jointly organized by Ningbo University and the Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources, the conference aimed to summarize and exchange the latest research achievements in the theories, methodologies, technologies, and applications of imaging spectroscopy for earth observation. It also delved into the in-depth applications of this technology in fields such as resource surveys, environmental protection, marine ecology, social development, and life sciences, jointly exploring new avenues for the development of imaging spectroscopy for earth observation technology.