A CIE Color Purity Algorithm to Detect Black and Odorous Water in Urban Rivers Using High-Resolution Multispectral Remote Sensing Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 57; no. 9; pp. 6577 - 6590
• Main Authors: Shen, Qian, Yao, Yue, Li, Junsheng, Zhang, Fangfang, Wang, Shenglei, Wu, Yanhong, Ye, Huping, Zhang, Bing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Atmospheric correction; Atmospheric measurements; Black and odorous water (BOW); Chromaticity; Color; color purity algorithm; Colour; Data; Detection; Error correction; Error detection; Gaofen (GF); Ground truth; High resolution; Image color analysis; Image resolution; Object recognition; Purity; Reflectance; Remote sensing; Resolution; Response functions; River water; Rivers; Satellites; Spatial discrimination; Spatial resolution; Spectral sensitivity; Sulfides; Sulphides; Suspended matter; Urban areas; visible bands; water color; Water pollution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2019.2907283

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance (<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>) in visible bands is 25.19%. We first measured <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data were converted into <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> corresponding to the wide GF-2 bands using the spectral response functions. We used the converted <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data to calculate several band combinations, including the baseline height, [<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">- R_{\mathrm {rs}} </tex-math></inline-formula>(red))/(<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">+ R_{\mathrm {rs}} </tex-math></inline-formula>(red)], and the color purity on a Commission Internationale de L'Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.