Rural Landscape Pattern Analysis and Optimization Model Construction Based on Remote Sensing Technology
Keywords:
Rural Area, Deep Learning, Classification, Optimization, Pattern, Multi-Spectral ImagesAbstract
Rural landscape pattern analysis involves the examination of the spatial arrangement and composition of land cover types in rural areas. in rural landscape pattern analysis encompass challenges such as data availability, scale discrepancies, and methodological complexities. Limited access to high-resolution spatial data, particularly in remote or developing regions, can impede accurate analysis and interpretation. Scale discrepancies between the spatial extent of data sources and the ecological processes being studied can also affect the reliability of findings. Hence, this paper proposes Genetic Optimized Stimulated Annealing Multi-Spectral (GSA-MS) for the pattern analysis. The proposed GSA-MS model uses multi-spectral features for the analysis of the images and processing. With the GSA-MS model features are extracted in the rural images for the estimation of patterns. With the GSA-MS model the features in the multi-spectral images are estimated and classified. The estimated features are optimized with the stimulated annealing model for the estimation and classification of patterns in rural images. Based on the computed and estimated features the LSTM-based deep learning model is implemented for the pattern classification in the rural area. By utilizing multi-spectral data, the model captures a broader range of information, enabling a more comprehensive analysis of rural landscapes. Specifically, the GSA-MS model optimizes the extracted features using a simulated annealing algorithm, which iteratively refines the feature set to improve pattern estimation and classification accuracy in rural images. Additionally, the paper proposes the integration of a Long Short-Term Memory (LSTM) based deep learning model for further enhancing pattern classification accuracy in rural areas. Simulation results demonstrated that the proposed GSA-MS model achieves a higher classification accuracy of 99% for the estimation of patterns in the images with a minimal loss of 0.09.
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