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

Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable

Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information

© 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations

An iterative interpolation deconvolution algorithm for superresolution land cover mapping

Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation, and then determines class labels of fine resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between observed coarse resolution fraction images and the latent fine resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms, and should be replaced by de-convolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation de-convolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse resolution fraction images with an area-to-area interpolation algorithm, and produces an initial fine resolution land cover map by de-convolution. The fine spatial resolution land cover map is then updated by re-convolution, back-projection and de-convolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multi-spectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors, and can preserve the patch continuity and the patch boundary smoothness, simultaneously. Moreover, the IID algorithm produced fine resolution land cover maps with higher accuracies than those produced by other SRM algorithms

Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM

Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of imagery acquired from current satellite sensors and as such various spatiotemporal image fusion methods have been explored to circumvent the challenges this situation presents (e.g., STARFM). However, some challenges persist in mapping surface water at the desired fine spatial and temporal resolution. Principally, the spatiotemporal changes of water bodies are often abrupt and controlled by topographic conditions, which are usually unaddressed in current spatiotemporal image fusion methods. This paper proposes the SpatioTemporal Surface Water Mapping (STSWM) method, which aims to predict Landsat-like, 30 m, surface water maps at an 8-day time step (same as the MODIS 8-day composite product) by integrating topographic information into the analysis. In addition to MODIS imagery acquired on the date of map prediction and a pair of MODIS and Landsat images acquired temporally close to the date of prediction, STSWM also uses the surface water occurrence (SWO, which represents the frequency with which water is present in a pixel) and DEM data to provide, respectively, topographic information below and above the water surface. These data are used to translate the coarse spatial resolution water distribution representation observed by MODIS into a 30 m spatial resolution water distribution map. The STSWM was used to generate an 8-day time series surface water maps of 30 m resolution in six inundation regions globally, and was compared with several other state-of-the-art spatiotemporal methods. The stratified random sampling design was used, and unbiased estimators of the accuracies were provided. The results show that STSWM generated the most accurate surface water map in which the spatial details of surface water were well-represented

Разработка фотографических методов оценки качества строительных материалов и изделий

Объектом исследования являются: алгоритмы обработки информации в методах оценки качества строительных материалов и изделий по их цифровым изображениям. Цель работы – исследовать возможные приложения фотографических методов в строительной отрасли и производстве строительных материалов и разработать ряд алгоритмов обработки цифровых изображений строительных материалов и изделий с целью оценки их качества.The object of the study are: information processing algorithms in methods of assessing the quality of building materials and products by their digital images. Purpose of work - investigate the possible applications the photographic techniques in the construction industry and production of construction materials and to develop a series of digital image processing algorithms for building materials and products in order to assess of quality

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Improved quantification of forest range shifts and their implications to ecosystem function in high-elevation forests

Rapid environmental changes are driving shifts in forest distribution across the globe with significant implications for ecosystem function and biodiversity. Despite the prevalence of forest range shifts across the globe, reliable estimations of changes in forest extent and structure at the elevational treeline (the elevational limit of forest distribution) are difficult to obtain due to limited access to mountainous environments. Remote sensing data is well suited to quantifying environmental change across large areas; however, a lack of published research that uses remotely sensed data in studies of mountain forests has led to uncertainty surrounding how much information about forest structure at the mountain treeline can be resolved in remotely sensed data. This uncertainty presents a major obstacle to landscape-scale quantification of forest range shifts and estimation of the impacts forest advance will have on ecosystem function and biodiversity in mountain systems. The distribution of high-elevation coniferous forests in the Central Mountain Range, Taiwan, has changed rapidly with increases in treeline elevation and forest density reported. Climate is considered to be the primary regulatory factor of the treeline in the Central Mountain Range. However, topography modifies the response of treeline advance to environmental change resulting in a structurally diverse treeline. This research combines a network of field observations across the Central Mountain Range, Taiwan, with aerial photography and multispectral satellite imagery to 1) determine which spectral features derived from multispectral satellite remote sensing best explain variation in mountain treeline structure and the effect of sensor spatial resolution on the characterisation of structural variation; 2) quantify variation in rates of forest advance; 3) quantify the accuracy of forest change assessments using a sample-based area estimation and classifying spectral trends identified in a time-series of satellite remote sensing data, and 4) quantify changes in above-ground woody biomass. The results presented here show that the green, red and short-wave infrared spectral bands and vegetation indices derived from these spectral bands offer the best characterisation of vegetation structure across the treeline ecotone with R2 values reported up to 0.723. Sample-based change assessment using repeat aerial photography shows a 295.0 ha increase in forest area and a 115.1 m increase in the mean elevation of forest establishment between 1963 and 2016. The rate of forest advance is spatially variable with forest establishment occurring most rapidly on east and south facing slopes with gradients of 0-20° and is also temporally variable with the rate of forest establishment peaking between 1980 and 2001. The classification of spectral trends in time-series analysis shows that Landsat-based change estimates underestimate the area of forest advance in the Central Mountain Range. However, the general pattern and direction of habitat change are consistent with those derived from sample-based estimates of change using repeat aerial photography offering the opportunity for error adjustment. Consequently, the results presented within this thesis show a net gain in above-ground woody biomass of 4688.7 t C in areas above 2400 m a.s.l. in the Central Mountain Range, Taiwan, and a reduction in the area of alpine grassland. The methods presented in this thesis provide a major opportunity to improve the quantification of forest range shifts across mountain systems allowing the estimation of landscape-scale impacts of forest advance on biodiversity and ecosystem function in data-poor mountain regions