Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A hierarchical method for subpixel surface water mapping accounting for the high spectral heterogeneity of urban materials is proposed in this paper. Specifically,  first applied water index (WI) for remote sensing image classification at pixel level, afterwards, the land, water, and land-water mixture can be extracted automatically. Then the spectral mixture analysis (SMA) is applied to land-water mixed pixels for water fraction estimation at subpixel level. To obtaining the most representative endmembers in SMA,  designed an adaptive iterative endmember selection method based on the spatial similarity of adjacent pixels. The proposed hierarchical processing method based on WI and SMA (WISMA) is applied to urban areas for reliability evaluation using the Landsat-8 Operational Land Imager (OLI) images. For comparison, four methods at pixel level and subpixel level re chosen respectively. Results indicate that the water maps generated by WISMA correspond as closely with the truth water maps with subpixel precision. And the results shod that the WISMA achieved the best performance in water mapping with comprehensive analysis of different accuracy evaluation indexes (RMSE and SE).
['Xin Luo', 'Huan Xie', 'Xiong Xu', 'Haiyan Pan', 'Xiaohua Tong']
I am writing a research paper on a idea A hierarchical processing method for subpixel surface water mapping from highly heterogeneous urban environments using Landsat OLI data
[{"from": "human", "value": "I am writing a research paper on a idea A hierarchical processing method for subpixel surface water mapping from highly heterogeneous urban environments using Landsat OLI data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A hierarchical method for subpixel surface water mapping accounting for the high spectral heterogeneity of urban materials is proposed in this paper. Specifically,  first applied water index (WI) for remote sensing image classification at pixel level, afterwards, the land, water, and land-water mixture can be extracted automatically. Then the spectral mixture analysis (SMA) is applied to land-water mixed pixels for water fraction estimation at subpixel level. To obtaining the most representative endmembers in SMA,  designed an adaptive iterative endmember selection method based on the spatial similarity of adjacent pixels. The proposed hierarchical processing method based on WI and SMA (WISMA) is applied to urban areas for reliability evaluation using the Landsat-8 Operational Land Imager (OLI) images. For comparison, four methods at pixel level and subpixel level re chosen respectively. Results indicate that the water maps generated by WISMA correspond as closely with the truth water maps with subpixel precision. And the results shod that the WISMA achieved the best performance in water mapping with comprehensive analysis of different accuracy evaluation indexes (RMSE and SE)."}]