Urban land-use classification by combining high-resolution optical and long-wave infrared images

• Main Authors: Xuehua Guan, Shuai Liao, Jie Bai, Fei Wang, Zhixin Li, Qiang Wen, Jianjun He, Ting Chen
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-10-01
• Series: Geo-spatial Information Science
• Subjects: Very high-resolution image; long-wave infrared image; combined imagery; multi-source data fusion; urban mapping; classification
• Online Access: http://dx.doi.org/10.1080/10095020.2017.1403731

Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWIR) images are analyzed separately and then combined for urban mapping in this study. The framework of its methodology is based on a two-level classification approach. In the first level, contributions of these two data sources in urban mapping are examined extensively by four types of classifications, i.e. spectral-based, spectral-spatial-based, joint classification, and multiple feature classification. In the second level, an objected-based approach is applied to decline the boundaries. The specificity of our proposed framework not only lies in the combination of two different images, but also the exploration of the LWIR image as one complementary spectral information for urban mapping. To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.