Application of optimized CNN algorithm in landslide boundary detection

Abstract

Landslide, as a natural geological phenomenon with great harm, seriously threatens human social activities and life safety. It has a variety of latent and immeasurable destructiveness, which has a significant impact on the economic losses in rural areas. Therefore, it is urgent to take measures to accurately identify landslides to reduce their negative impacts. However, traditional manual visual interpretation has been unable to meet the current needs for emergency rescue of landslides, so computer intelligent methods have been paid attention to. This study proposes a new recognition network to address the problem of low accuracy of intelligent landslide boundary recognition methods. Firstly, the experiment incorporated boundary structure information into the Full Convolutional Network (FCN) for optimization, and constructed an Improved Full Convolutional Network (IFCN) model to better achieve image reconstruction. After that, Attention Mechanism (AM) is further introduced to achieve accurate detection of landslide boundaries in images, namely the IFCN-AM model. The attention mechanism introduced include spatial attention mechanism and multi-channel attention mechanism. Both are responsible for enhancing the language representation ability of the model and aggregating the interrelated features between different channels. The experimental results show that IFCN-AM has a 3 % to 7 % improvement in accuracy, recall, F1 value, and MIoU value.

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

“…Except the mentioned works, many state-of-the-art learning techniques, including multiclass classification [15] and few-short learning [16], are also found effective. Nevertheless, pixel or object-level detection has a large computational burden, and [17] points out in many scenarios, the accurate identification of landslide boundary is challenging. In contrast with pixel-level segmentation, imagelevel landslide classification is able to differentiate landslide and other non-landslide scenes according to geological and visual features, such as object colors, textures and topographic patterns, from the level of image labels.…”