Work

Fusion of VNIR and thermal infrared remote sensing data based on GA-SOFM neural network

Year: 2009

Type: article

Abstract: The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR (visible and near infrared) data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM (genetic algorithms & self-organizing feature maps)-ANN (artificial neural network). Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.

Source: Geo-spatial Information Science

DOI: 10.1007/s11806-009-0093-4

Authors: Chongchang Wang, Guijun Yang, Zhen Ma, Zhurong Xing

Institution: Liaoning Technical University

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,993

Funders: National Natural Science Foundation of China, Major State Basic Research Development Program of China, National High-tech Research and Development Program, National Key Research and Development Program of China