Abstract: The study delves into the landscape feature identification method and its application in Xijingyu Village, investigating landscape composition elements. Analyzing rural landscape structure holistically aids in dividing landscape characteristic zoning maps, essential for guiding rural landscape and territorial spatial planning. By utilizing GIS software for superposition analysis based on topography, geology, vegetation cover, and land use, the village range of west well valley undergoes further refinement. To address the inefficiencies of common foreground extraction algorithms relying heavily on rural landscape images, a novel approach is introduced. This new algorithm focuses on directly extracting foreground areas from rural landscape interference images by leveraging stripe sinusoidal characteristics. An adaptive gray scale mask is established to capture the sinusoidal changes in interference stripes, facilitating the direct extraction of foreground areas through a calculated blend of masks. In evaluating the results, the newly proposed algorithm demonstrates significant improvements in operation efficiency while maintaining accuracy. Specific enhancements include classifying pixel gray values into intervals and recalibrating them to enhance analysis metrics. Compared to traditional methods, the algorithm showcases advantageous enhancements across various parameters, such as PRI, GCE, and VOI. Moreover, to address challenges in unwrapping low-quality rural landscape phase areas, a ResU-net convolutional neural network is employed for phase unwrapping. By constructing image datasets of interference stripe wrapping and unwrapping alongside noise simulations for model training, the network structure's feasibility is verified. The study's innovative methodologies aim to optimize rural landscape analysis and planning processes by enhancing accuracy and efficiency in landscape feature identification, foreground area extraction, and phase unwrapping of rural landscapes. These advancements offer substantial improvements in quality and precision for territorial spatial planning and rural landscape management practices.

Ling Sun, Jun Liu, Yi Qu, Jiashun Jiang and Bin Huang, “Automatic Identification and Evaluation of Rural Landscape Features Based on U-net” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150804

@article{Sun2024,
title = {Automatic Identification and Evaluation of Rural Landscape Features Based on U-net},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150804},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150804},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Ling Sun and Jun Liu and Yi Qu and Jiashun Jiang and Bin Huang}
}