نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه فتوگرامتری و سنجش از دور، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشکدگان فنی، دانشگاه تهران، تهران، ایران
2 گروه فتوگرامتری و سنجش از دور، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشکدگان فنی، دانشگاه تهران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Floods are one of the natural hazards that occur in many parts of the world and cause irreparable financial and human losses. One of the most important aspects of controlling this crisis is the accurate identification of flooded areas. Also, it is important to predict flood-prone areas in order to prevent and reduce losses and casualties related to flooding. In this article, with the help of Sentinel-1 satellite images and a deep learning encoder-decoder network, the flood phenomenon in the images has been identified and segmented. These images belong to the regions of Nebraska, North Alabama, Bangladesh, Red River North, and Florence, and the ground truth map of each image, in which the target and non-target classes are shown as 0 and 1, was provided by NASA in 2021. In this article, flood-affected areas have been identified and segmented using encoder-decoder convolutional neural networks and the aforementioned satellite images. This network consists of encoder and decoder paths each containing convolutional layers which are responsible for extracting features and recovering these features, respectively. Various evaluation criteria were used to evaluate the performance of this method, including accuracy, IoU, F1-Score, and Kappa. This method has shown very good performance in the process of identifying and segmenting flooded areas based on the results obtained. The IoU obtained during the evaluation process was 96.04%, which is higher than the highest IoU obtained in other comparable studies (76.81%).
کلیدواژه‌ها [English]
)