@article { author = {Aslani Katouli, Somayeh and Shah-Hosseini, Reza and Bagheri, Hamid}, title = {Urban Flood Mapping using SAR Intensity Images and Coherence Interference - Case Study: Flood Event of Gonbad-e-Kavus}, journal = {Scientific- Research Quarterly of Geographical Data (SEPEHR)}, volume = {}, number = {}, pages = {-}, year = {2023}, publisher = {National Geographical Organization}, issn = {2588-3860}, eissn = {2588-3879}, doi = {10.22131/sepehr.2023.553468.2874}, abstract = {Extended Abstract Introduction A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on a convolutional neural network is introduced, and from the time series of SAR intensity and coherence to draw flood without obstruction (e.g. Flooded bare soils and short vegetation) are used. Non-cohesive areas blocked by floods (e.g., flooded vegetation) and cohesive areas with flood-blocked areas (e.g., frequently constructed flooded areas) are distinguished. This method is flexible according to the time period of the data sequences (at least one pair of pre-event and event intensities and one pair of pre-event and in-event coherence are required). The increasing number of SAR missions in orbit that have a fixed viewing scenario with a short retry time increases the chances of seeing a flood event, while also having a good pre-event scene achieved by the same sensor. This makes this method desirable for operational emergency responses. Materials & Methods CNN algorithm is a multilayer perceptron that is designed to identify two-dimensional information of images and includes: input layer, convolution layer, sample layer, and output layer. The CNN algorithm has two main processes: collection and sampling. The convolution process involves the use of a trainable Fx filter, deconvolution of the input image (the first step of image input, input after image convolution, is the feature of each layer called Feature Map), then by adding bx can be hand convolution of the CX layer Found. Sampling process: n pixels are collected from each neighborhood to form a pixel, then weighted with a scalar weight of Wx + 1 and a bx + 1 bias is added, then a map of The Narrow n times feature map properties are generated. Three images of Sentinel-1A VV polarization, wide width interference (IW), and mode (SLC) data were used in this study. Intensity images were pre-processed with radiometric calibration, noise reduced with a spell-filter (window size 5.5 pixels), and converted from linear units to decibels. Coherent images were obtained with a pair of consecutive images with a window of 7.28 (range _ azimuth). Validation data set due to the lack of other data in two separate sections of ground data in the urban area of GonbadKavous that have been collected to identify homes damaged by floods and terrestrial reality data from gamma image thresholds for output validation were extracted. Results & Discussion In this section, the results of the study are qualitatively and quantitatively analyzed. Because the simultaneous display of SAR data over time in the form of RGB compounds is widely used in the qualitative interpretation of land cover and surface dynamics, RGB compounds are used to provide evidence of flood magnitude in terms of intensity and coherence. For both cases, the results of combining intensity and coherence and intensity alone and coherence alone are quantitatively analyzed. Overall accuracy (OA), kappa correlation coefficient, false-positive rate (FPR), precision (e.g., correctly predicted positive patterns out of the total predicted patterns in a positive class), recall (e.g., a fraction of properly classified positive patterns), and an F1 score (ie the harmonic mean between precision and recall). Flood reference and ground data are mentioned and reported based on the reference. Conclusion In this paper, a method for mapping floods in urban environments based on SAR intensity and interferometry coherence was introduced. A combination of intensity and coherence extracts flood information in different types of land cover and outlet. This method was tested on the KavousGonbad flood incident obtained by various SAR sensors and the flood maps were confirmed by the flood reference resulting from thresholding and ground harvesting and satisfactory results were shown in this case study. The findings of this experiment show that the shared use of SAR intensity and coherence provides more reliable information than the use of SAR intensity and coherence alone in urban areas with different landscapes. In particular, flood detection in less cohesive / non-cohesive areas (e.g., bare soils, vegetation, vegetated areas) relies heavily on multi-temporality, while multi-temporal coherence provides more comprehensive flood information in areas Create coherence (e.g., mostly built-up areas). However, some flood-specific situations, such as flooded parking lots and flooded dense building blocks, are still challenging in terms of intensity and coherence. Also, since the proposed method is sensor and scene independent, with very frequent and regular observations of SAR missions such as Sentinel-1 and RADARSAT (RCM), there are opportunities to map global floods on a global scale, especially in small countries. Provides income.}, keywords = {Urban Flood Mapping,SAR Multi-Time Images,InSAR coherence,Convolution neural network}, title_fa = {تهیه نقشه سیلاب شهری با استفاده از تصاویر شدت SAR و همدوسی تداخل سنجی - مطالعه موردی: سیل گنبد کاووس}, abstract_fa = {سیل یکی از مخاطرات طبیعی است که می­ تواند به شدت بر زندگی انسان تأثیر بگذارد، به گونه­ ای که برای واکنش اضطراری به آن نیاز به ارزیابی دقیق منطقه آسیب دیده پس از حادثه می­ باشد. مشاهدات رادار با روزنه مجازی (SAR) بطور گسترده در تهیه نقشه و نظارت بر سیل استفاده می­ شود. با این حال، خدمات عملیاتی فعلی عمدتاً معطوف به سیل در مناطق روستایی است و مناطق سیل زده شهری، کمتر مورد توجه قرار می­ گیرند. در عمل، نقشه ­برداری از سیلاب­ های شهری به دلیل مکانیسم­ های پیچیده برگشت در محیط ­های شهری، چالش برانگیز است و علاوه بر شدت SAR، اطلاعات دیگری نیز لازم است. در این مقاله یک روش طبقه ­بندی برای تشخیص سیل در مناطق شهری با تلفیق استفاده از شدت SAR و همدوسی تداخل­ سنجی تحت چارچوب شبکه عصبی کانولوشن CNN معرفی می ­شود، تا اطلاعات سیل از مناظر مختلف را استخراج نماید. به منظور تمایز تغییرات حاصل از سیلاب از دیگر تغییرات، از سه سری زمانی همدوسی حاصل از تصاویر(قبل ـ قبل، قبل ـ بعد و بعد ـ بعد) استفاده شده است. این روش در رویداد سیل گنبد کاووس  با داده­ های Sentinel-1 آزمایش می­ شود. نقشه­ های سیلاب حاصل از تلفیق شدت و همدوسی و شدت به تنهایی در مقایسه با داده ­های کنترل زمینی در مناطق شهری و داده­ های حاصل از آستانه­ گذاری تصاویر سنتینل-1 نشان می­ دهد که دقت کلی 93.8٪ و ضریب کاپا 0.81 برای ترکیب شدت و همدوسی و نیز دقت کلی 90.6٪ و ضریب کاپا 0.72 برای ترکیب شدت به تنهایی و دقت کلی 86.8٪ و ضریب کاپا 0.56 برای ترکیب همدوسی به تنهایی وجود دارد. آزمایشات نشان می ­دهد که همدوسی علاوه بر شدت در تهیه نقشه از سیلاب شهری، اطلاعات ارزشمندی را فراهم می­ کند و روش پیشنهادی می­ تواند ابزاری مفید برای تهیه نقشه از سیلاب شهری باشد.}, keywords_fa = {تهیه نقشه سیلاب شهری,تصاویر چند زمانه SAR,InSAR coherence,شبکه عصبی کانولوشن}, url = {https://www.sepehr.org/article_703872.html}, eprint = {} }