Research on segmentation algorithm of UAV remote sensing image based on deep learning

Abstract

With the rapid development of UAV technology, the research topic of remote sensing image segmentation has gradually attracted more and more attention. Whether the image can be accurately segmented is a measure of the goodness of the algorithm. In recent years, machine learning methods have been applied to a large number of fields, and deep neural network technology has also been widely used in the field of UAV remote sensing image segmentation. This paper introduces the specific applications of various deep learning methods in remote sensing image segmentation, and briefly analyzes the development of neural networks in this problem according to the research status of several typical deep neural networks in UAV remote sensing image segmentation. Research shows that on the basis of proper adjustment of optimizer, learning rate and loss function, using deep learning method to segment UAV remote sensing images, the accuracy rate can reach more than 96%.

UTFormer: A UAV Sensing-based Triple Attention Transformer Network for Semantic Segmentation

“…The rapid development of UAV technologies, coupled with advancements in sensor capabilities, has facilitated detailed and accurate data collection over specific areas of interest, making UAVs an indispensable asset for both research and practical applications in remote sensing [6].…”

“…The precise and efficient segmentation of images captured by UAVs is crucial for extensive future applications, including urban planning [2,3], disaster assessment [4], environmental monitoring [5], etc. However, achieving swift and accurate segmentation in complex and large-scale UAV scenes remains a numerous challenge [6]. The evolution of semantic segmentation has transitioned from reliance on traditional methods like edge detection, thresholding, and region-based techniques to embracing the capabilities of deep learning [7].…”