استفاده از مدل زیر پیکسل جاذبه به منظور افزایش قدرت تفکیک مکانی مدل رقومی ارتفاع (dem)

افزایش قدرت تفکیک مکانی به منظور افزایش میزان اطلاعات در مدل رقومی ارتفاع (dem) از جمله مهمترین موضوعات در ژئومورفولوژی کمی محسوب می‌شود. تاکنون مدل‌های مختلفی به منظور افزایش قدرت تفکیک مکانی ارائه شده است که از بین مدل‌ها، مدل جاذبه به عنوان جدیدترین مدل، دارای دقت بسیار بالایی می‌باشد. این مدل برای اولین بار به منظور افزایش قدرت تفکیک مکانی بر روی تصاویر ماهواره‌ای استفاده شده است. در این تحقیق از مدل جاذبه برای اولین بار به منظور افزایش قدرت تفکیک مکانی dem استفاده شد. در بررسی حاضر، از دو مدل همسایگی پیکسل‌های مماس (touching) و مدل همسایگی چهارگانه (quadrant) به منظور تخمین مقادیر زیر پیکسل ها استفاده گردید. در مدل جاذبه احتیاجی به کالیبره کردن و آموزش الگوریتم همانند الگوریتم‌های یادگیری ماشین نیست، این امر موجب می‌شود که زمان محاسبات برای اجرای الگوریتم کم شود. پس از تولید تصاویر خروجی برای زیر پیکسل‌ها، در مقیاس های 2، 3 و4 با همسایگی‌های متفاوت، بهترین مقیاس با مناسب‌ترین نوع همسایگی با استفاده از نقاط کنترل زمینی تعیین شد و مقادیر rmse برای آن‌ها محاسبه شد. تعداد کل نقاط کنترل زمین مستخرج از عملیات نقشه برداری، 2118 نقطه بود. مقدار rmse برای هر dem به صورت جداگانه محاسبه شد. نتایج نشان داد که با استفاده از مدل جاذبه صحت تصاویر خروجی بهبود بخشیده شده و همچنین قدرت تفکیک مکانی آن‌ها نیز افزایش پیدا کرده است. بر اساس نتایج از بین مقیاس‌ها با همسایگی‌های مختلف، مقیاس 3 و مدل همسایگی چهارگانه نسبت به سایر روش‌ها دارای بیشترین دقت با کمترین میزان rmse (54/5) برای dem 30 متر و dem  90 متر (13/9) می‌باشد.

Using subpixel/pixel spatial attraction model to increase spatial resolution of DEM

Topography is a factor controlling the spatial distribution of soil moisture, vegetation, soil salinity, soil texture and so on. It has an important role in changing the characteristics of the soil and hydrological processes. In recent years have been used the topography as an important factor for predicting the properties of soil, climate, geology, etc. According to the importance of topography to extract different information, use of satellite images with high spatial resolution seems very necessary. Digital elevation models (DEM) have become a widely used tool and product in the last 20 years. They provide a snap shot of the landscape and landscape features while also providing elevation values. They have allowed us to better visualize and interrogate topographic features.  In addition to increasing the spatial resolution increase information of the digital elevation model (DEM) that is the most important issues in quantitative geomorphology. In order to increase the spatial resolution have been proposed several models. Among the models, the attraction model as the newest model has very high accuracy. The subpixel attraction models convert the pixel towards subpixels based on the fraction values in neighboring pixels that can be attracted only by central pixel. Based on this approach only a maximum of eight neighboring pixels can be selected for the attraction. In the model other pixels are supposed to be far from the central pixel to have any attraction. In this study by using subpixel attraction model the spatial resolution of digital elevation models (DEM) was increased (Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery). The design of the algorithm is accomplished by using digital elevation model (DEM) with spatial resolution of 30 m (Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER)) and 90 m (Shuttle Radar Topography Mission (SRTM)). This study was carried out in the East Mount Sahand, Iran and is located at longitude of N 37° 31َ  to 37° 30َ  and latitude of E 45° 55َ  to 45° 58َ  . It is expected that using attraction model increased spatial resolution DEM. The attraction model don’t need to calibration and training similar the machine learning algorithms. So in the model reduced computing time to run of the algorithm. In attraction model scale factors of (2, 3 and 4) with two neighboring methods of touching and quadrant are applied to DEMs using Matlab software and then using RMSE (Root mean square error) determined the best model. The algorithm is evaluated using 2118 sample points that are measured by surveyors. As the result of Root mean square error (RMSE), it showed that the spatial attraction model with scale factor of (S=2 and T=2) for digital elevation model (DEM) 30m and digital elevation model (DEM) 90m gives better results compared to scale factors that are greater than 2 and also touching neighborhood method proved to be more accurate then quadrant. In fact, subtracting each pixel to more than two subpixels caused to decrease the accuracy of resulted DEM which makes to increase the value of root mean square error (RMSE) and showed that attraction models could not be used for S which is greater than 2. So according the results is suggested that used the model for increasing spatial resolution of DEM in the studies catchment. The comparing of digital elevation model (DEM) as inputs in the attraction models determined that digital elevation model (DEM) 30 m (root mean square error < 5.54) has better spatial resolution than digital elevation model (DEM) 90 m (root mean square error = 9.13) to find the best model for increasing spatial resolution. The results showed that using the method the spatial resolution of digital elevation model (DEM) with lower time and cost, could be increased. Digital elevation model (DEM) map with high resolution as based can be used for finding more information from earth surface. For different study such as amount of vegetation, temperature, rainfall and hydrological status can use results of subpixel attractions on digital elevation model (DEM) and find more details of study area. Therefore it is suggested same researches should be done in other areas with different topographic and geographical conditions in order to confirm the results of this study.