Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However, owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this study, two Landsat 8 Operational Land Imager (OLI) images—acquired on 20 September 2019 (Path/Row: 121/44) and 14 November 2019 (Path/Row: 122/44)—of Guangzhou and Shenzhen were unmixed by LSMA using spectral indices in endmember selection. A post-processing model using the Dry Bare-soil Index (DBSI) and Normalized Difference Vegetation Index (NDVI) as thresholds was established to improve the IS fraction of the LSMA result. Comparative analysis reveals that LSMA with the post-processing model achieves better performance for IS fraction extraction (R ² = 0.910 and 0.926 and root mean square error [RMSE] = 10.08% and 10.83% for Guangzhou and Shenzhen, respectively), and the distribution of IS is basically consistent with the IS of the actual areas. The post-processing model solves the problem of overestimation of pervious surface and underestimation of impervious surface.

The mismatch between a spatial-domain encoder and a transform-domain decoder due to rounding can cause serious video quality degradation after a series of inter-coded frames. The mismatch is rooted on the fact that rounding is a nonlinear operation, and hence its equivalent in the transform domain does not exist. We analyze the mathematical property of the rounding operation and propose a practical solution that provides an optimal approximation of the rounding operation in the transform-domain. The proposed solution is able to reduce the mismatch and preserve image quality. Experimental results are shown to demonstrate the effectiveness of the solution

In technologically underdeveloped areas, water pollution threatens the living environment of local residents, so remote sensing monitoring of the features around reservoirs is necessary. Fully convolutional networks (FCNs) offer great potential for extracting high-resolution features due to their unlimited input image size and higher accuracy compared to convolutional neural networks. Therefore, a proposal to classify WorldView-2 images is implemented with a sixty-eight thousand iterations of fine-tuning and fully trained combined training method based on a fully convolutional network (SEFCN). The chosen images depict the urban area of Yingde, which is located to the northeast of the Feilaixia Reservoir, Qingyuan, Guangdong Province, China. The SEFCN combines an FCN-32s and FCN-16s to better integrate the deep features and shallow features and effectively improve the classification accuracy. Additionally, the loss value fully converges with enough iterations, and the overfitting caused by the superposition of two full trainings is avoided. The SEFCN model achieves the highest accuracy among all compared classification models and obtains the best classified results on the WorldView-2 images, as demonstrated by attaining the highest F1 scores in each category. After the classified images are optimized using a conditional random field, the current status of the study area is analyzed, and several suggestions for land use in the urban area of Yingde are made. The experiments still have deficiencies in the application of high-resolution remote sensing image classification with different sensors, and the classification results can be optimized and improved in other aspects.