Application of remote sensing image processing based on artificial intelligence in landscape pattern analysis

Abstract

The spatial arrangement of various land cover types within a landscape is referred to as the Landscape Pattern (LSP). An essential component of landscape ecology, LSP examination is significant for a variety of causes, like species conservation, sustainable development, environmental monitoring, landscape planning, and management of accepted resources. The development of Remote Sensing (RS) images permits urban planners to additional systematically and economically. 0 identify the land use of a specified area on a slighter time scale. The objective of this study is to build up an artificial intelligence (AI)-based RS image processing performance for LSP. This study, proposed a novel refined flamingo search-dynamic recurrent neural network (RFS-DRNN) to analyze the LSP. RS image data were gathered from landscape characteristics. The Discrete Wavelet Transform (DWT) utilizes pre-processed data to eliminate noise, though maintenance is important distinctiveness. Convolutional Neural Network (CNN) using extracted features from image data. RFS could be used to progress the constraint of a DRNN model that is used to analyze patterns in the landscape. It can be used to regulate an RNN's hyper parameters to enhance its ability to recognize and categorize landscape features. The results showed that the proposed method is effective at analyzing LSPs. The significance indicates that the proposed method has achieved superior performance in including accuracy [98.90%], precision [94.82%], recall [93.75%], and F1-score [95.29%]. The hierarchical land-cover mapping reveal process creates thorough LSP analysis possible by using satellite images and sophisticated algorithms. High training accuracy and decreasing training loss indicate effective model learning and generalization for landscape analysis. The execution times at the end highlight the important it is to maximize processing methods and computational capacity to build quick decisions while analyzing LSP.