Algorithm of adaptive correction of structural landforms on global digital elevation models

2018 ◽  
Vol 937 (7) ◽  
pp. 57-64
Author(s):  
A.Y. Zhdanov ◽  
A.V. Pankin ◽  
A.V. Rentel
Keyword(s):  
High Altitude ◽  
Digital Elevation Models ◽  
Reference Network ◽  
Height Difference ◽  
Second Stage ◽  
Digital Elevation ◽  
Adaptive Correction ◽  
Dem Accuracy ◽  
Two Stages

Due to various factors, such as the interpolation step or automatic correlators specifics, global digital elevation models (DEM) often have an effect of understating the heights, which leads to inaccurate display of structural landforms e.g. ridges. The algorithm of adaptive correction of structural landforms elevation on DEM is proposed in this article. The algorithm consists of two stages. In the first stage, an automatic classification of structural forms is performed based on height difference between neighboring DEM elements. In the second stage, the DEM elements are corrected based on the assigned classes. Adaptivity of the algorithm allows to use it for any kind of terrain and elevation ranges. The algorithm was tested on the global DEM ALOS World 3D (ALOS W3D30); the accuracy was assessed by geodetic reference network and ICESat mission data. The developed algorithm shows an improvement of DEM accuracy, especially in high-altitude areas, and it also helps to reveal areas requiring additional verification.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Accuracy Evaluation of Four Greenland Digital Elevation Models (DEMs) and Assessment of River Network Extraction

2020 ◽  
Vol 12 (20) ◽  
pp. 3429
Author(s):  
Ziyang Xing ◽  
Zhaohui Chi ◽  
Ying Yang ◽  
Shiyi Chen ◽  
Huabing Huang ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Digital Elevation Models ◽  
Greenland Ice Sheet ◽  
River Network ◽  
The Other ◽  
Time Range ◽  
Accuracy Evaluation ◽  
Digital Elevation ◽  
Dem Accuracy ◽  
The Impact

Digital Elevation Models (DEMs) of Greenland provide the basic data for studying the Greenland ice sheet (GrIS), but little research quantitatively evaluates and compares the accuracy of various Greenland DEMs. This study uses IceBridge elevation data to evaluate the accuracies of the the Greenland Ice Map Project (GIMP)1 DEM, GIMP2 DEM, TanDEM-X, and ArcticDEM in their corresponding time ranges. This study also analyzes the impact of DEM accuracy and resolution on the accuracy of river network extraction. The results show that (1) within the time range covered by each DEM, TanDEM-X with an RMSE of 5.60 m has higher accuracy than the other DEMs in terms of absolute height accuracy, while GIMP1 has the lowest accuracy among the four Greenland DEMs, with an RMSE of 14.34 m. (2) Greenland DEMs are affected by regional errors and interannual changes. The accuracy in areas with elevations above 2000 m is higher than that in areas with elevations below 2000 m, and better accuracy is observed in the north than in the south. The stability of the ArcticDEM product is higher than those of the other three DEM products, and its RMSE standard deviation over multiple years is only 0.14 m. Therefore, the errors caused by the applications of DEMs with longer time spans are smaller. GIMP1 performs in an opposite manner, with a standard deviation of 2.39 m. (3) The river network extracted from TanDEM-X is close to the real river network digitized from remote sensing images, with an accuracy of 50.78%. The river network extracted from GIMP1 exhibits the largest errors, with an accuracy of only 8.83%. This study calculates and compares the accuracy of four Greenland DEMs and indicates that TanDEM-X has the highest accuracy, adding quantitative studies on the accuracy evaluation of various Greenland DEMs. This study also compares the results of different DEM river network extractions, verifies the impact of DEM accuracy on the accuracy of the river network extraction results, and provides an explorable direction for the hydrological analysis of Greenland as a whole.

Automatic high-resolution mapping and classification of avalanche terrain regarding potential release, triggering and run-out zones

2020 ◽  
Author(s):  
Stephan Harvey ◽  
Günter Schmudlach ◽  
Yves Bühler ◽  
Dürr Lukas ◽  
Andreas Stoffel ◽  
...  
Keyword(s):  
High Resolution ◽  
Digital Elevation Models ◽  
Density Estimate ◽  
Swiss Alps ◽  
Validation Data ◽  
Calculated Density ◽  
Digital Elevation ◽  
Remote Triggering ◽  
Terrain Characteristics

<p>Terrain characteristics are one of the main factors contributing to avalanche formation as well as affecting the runout. Hence, terrain assessment is crucial for planning and decision making when travelling in the backcountry. So far, terrain is mainly interpreted manually from topographic maps or by observations in the field. Recent support for interpreting avalanche terrain is given by slope angle layers derived from digital elevation models or the Avalanche Terrain Exposure Scale (ATES) for classifying avalanche terrain manually. While digital elevation models and numerical simulations are used as standard for mapping avalanche hazard threatening settlements and key infrastructure, this is hardly the case when planning tours in the backcountry. Thus, our scope was to classify and map terrain of maximum size class 3 avalanches, which typically threaten backcountry recreationists. We present a new methodology for a high-resolution automatic classification of the avalanche terrain specifically for recreational backcountry travel by taking into account: a) potential avalanche release areas, b) remote triggering of avalanches, c) possible runout zones of max. size 3 avalanches.</p><p>Potential release areas were specified by computing a density estimate based on terrain characteristics of observed avalanche starting zones in the Davos region. The potential of remote triggering was estimated with a least-cost path analyses depending on the triggering distance from remotely triggered avalanches. Avalanche runout zones were performed with the avalanche simulation model RAMMS::EXTENDED. Combining all these methods and out of many simulations a classified avalanche terrain map for the entire Swiss Alps and the Jura was created characterizing potential release areas and runout zones. A validation of 870 accidental avalanches in the backcountry of Switzerland shows that only 2% of the mapped avalanche perimeters do not overlap with the simulations. The distribution of the terrain characteristics within both the release areas of the training dataset and the validation data was almost identical. Thus, the extrapolation from the calculated density estimate to the whole of Switzerland is feasible and appropriate. The created map assists the interpretation of avalanche terrain for travelling in the backcountry considering release areas and runout zones. Although the focus is on Switzerland, the methods can also be applied to other mountain areas worldwide.</p>

scholarly journals Predictions in avalanche forecasting

2000 ◽  
Vol 31 ◽  
pp. 377-381 ◽  
Author(s):  
D. M. McClung
Keyword(s):  
Field Data ◽  
Informational Entropy ◽  
Avalanche Forecasting ◽  
The Public ◽  
Second Stage ◽  
Relevant Field ◽  
Verification Of Models ◽  
Using Data ◽  
Two Stages

AbstractVerification of avalanche forecasts depends on the spatial and temporal scale of the forecast, and the classes of informational entropy of data implicit in the forecast. First I present a classification system for avalanche forecasts based on these parameters. Verification of models in avalanche forecasting may consist of two stages. Often, the first stage is to ensure that the model matches the scales (space and time) and the classification of forecast and that redundant variables and parameters are eliminated. Once that is achieved, verification can proceed to the second stage, testing the model against relevant field data and situations. I provide an example based on the public-danger scale bulletin used for warnings in the back country in North America and Europe. Using data on deaths and accidents from Alpine Europe with Bayesian statistics, I conclude the danger scale has more classes than necessary for back-country applications. This could be a first stage prior to actual verification of this experience-based model.

scholarly journals Utilização de cinco bases cartográficas distintas para a determinação de áreas de preservação permanente no município de Rio Acima, MG: divergências en-tre os resultados / Using five different cartographic bases for determining permanent preservation area

2016 ◽  
Vol 26 (1) ◽  
pp. 136
Author(s):  
Felipe Silva Guimarães ◽  
Lucas Da Silva Guimarães
Keyword(s):  
Digital Elevation Models ◽  
Minas Gerais ◽  
Metropolitan Region ◽  
Second Stage ◽  
Digital Elevation ◽  
Minas Gerais State ◽  
Vector Base ◽  
The Law ◽  
Belo Horizonte ◽  
The City

<p>O estudo foi realizado no município de Rio Acima, localizado ao sul da região metropolitana de Belo Horizonte - MG. Na primeira etapa, foi feita a determinação das áreas de preservação permanente (APPs) do município, segundo a Lei 12.651 e a resolução CONAMA 303/2002 (esta última para delimitar as APPs de topo de morro), utilizando cinco bases topográficas distintas: vetorização de cartas do IBGE na escala de 1:50.000, imagem SRTM, Topodata, ASTER V2 e uma base vetorial disponibilizada pelo Codemig com curvas de nível equidistantes em 10 metros. Na segunda etapa, foram calculadas as áreas de cada uma das classes de APP resultantes dos mapeamentos feitos a partir de todas as bases e, em seguida, estes valores foram comparados. Ao final, foi observado que os resultados obtidos a partir da base do IBGE e da imagem ASTER V2 são os que apresentam característica mais restritiva, ou seja, com maiores áreas de preservação. Por outro lado, o mapa de áreas de preservação permanente confeccionado a partir da imagem SRTM foi o que apresentou caráter menos restritivo. Neste estudo também são discutidas outras diferenças entre os mapas elaborados a partir das cinco bases. </p><p> </p><p><strong>Palavras–chave:</strong> Município de Rio Acima, áreas de preservação permanente, modelos digitais de elevação, resolução espacial.</p><p><strong> </strong></p><p><strong> </strong></p><p><strong>Abstract </strong></p><p>This study was conducted in the municipality of Rio Acima (Minas Gerais State) located in the southern Belo Horizonte metropolitan region. In the first stage five different topographic bases were used to lay down the city permanent preservation areas (PPA) according to the Law 12,651 and CONAMA resolution 303/2002 (the latter to delimit the hilltop’s PPAs PPAs). These bases are the following: the vector at a 1:50.000 scale provided by the Brazilian Institute of Geography and Statistics (IBGE), SRTM image, image provided by INPE Topodata project, ASTER V2 Image and a vector base released by Codemig with 10 meters contour distance. In the second stage the areas of each one PPA classes were calculated resulting from all mapping bases. Then these values were compared. Finally it was observed that the results obtained from the ASTER V2 image and IBGE base are those with more restrictive features or in other words the result with lager PPAs. Moreover, the map of permanent preservation areas made from SRTM image showed the least restrictive character. This study also discusses other differences between the maps drawn from the five bases.</p><p><strong> </strong></p><p><strong>Keywords</strong>: Rio Acima municipality, permanent preservation areas, digital elevation models,   spacial resolution.</p>

Automatic classification of plants based on information content

1970 ◽  
Vol 48 (4) ◽  
pp. 793-802 ◽  
Author(s):  
Laszlo Orloci
Keyword(s):  
Information Theory ◽  
Cluster Analysis ◽  
Information Content ◽  
Random Sample ◽  
Nearest Neighbor ◽  
Theory Model ◽  
Second Stage ◽  
Large Samples ◽  
Two Stages

An information theory model is described and its application is illustrated by an actual example. Classification is accomplished in two stages. The first stage includes cluster analysis of a random sample by an agglomerative method. Cluster analysis is followed by nearest neighbor sorting in the second stage whereby the clustering results are imposed on a second random sample of the same collection. The advantage of the procedure resides in the fact that large samples can be handled, and also, the classification produced in the second stage can be used, under specific restrictive assumptions, for unbiased prediction of different population properties. While the present paper is principally concerned with the technique itself, some taxonomic conclusions are also given.

Hierarchical Classification Of Expert Models Of Exercises Designed To Eliminate Specific Mistakes Occurring In Short Ski Turn

2020 ◽  
Author(s):  
Danijela Kuna ◽  
Matej Babić ◽  
Mateja Očić
Keyword(s):  
Hierarchical Classification ◽  
Vertical Position ◽  
Chi Square ◽  
Second Stage ◽  
Expert Model ◽  
Significant Difference ◽  
Hands On ◽  
Chi Square Test ◽  
Two Stages

The aim of the present study was to examine the structure of an expert model of exercises designed to eliminate the Lack of specific ski movement mistake in short ski turn, as well as offer a hierarchical classification of the expert model. For this purpose, a two-stage research was conducted. During the first stage of the research the exercises with the purpose of Lack of specific ski movement mistake elimination were designed by 20 skiing experts aged 25 to 45. By means of email and coordinated by the paper author, the experts first designed a model of 14 methodical exercises and subsequently selected the five most relevant ones, ranking them on a scale from 1 to 5. A nonparametric chi - square test (χ2) was used. The research showed there was no significant variation across the experts’eval-uation of the five most important methodical exercises (χ2 = 21,69; p = 0,06). The expert model of the most important methodical exercises for the Lack of specific ski movement mistake correction thus includes the following: Holding a ski stick under the handle, Jump turns, Hands on hips, Unbuttoned ski boots and Ski poles in vertical position in forwards. 307 skiing professionals of various levels of expertise participated in the second stage of the research, whose aim was to classify the Lack of specific ski movement mistake elimi-nation exercises. The participants’task was to rank the exercises based on their relevance. Total amounts of rank sums (ΣR) were calculated, the Kruskal-Wallis test (H-test) was car-ried out, and the corresponding levels of significance (p) were recorded, for the purpose of comparing the significance of diversity between rank sums and the expert model. The sta-tistically significant difference was found between the rank sums (ΣR) of the most eficient exercises for the Lack of specific ski movement mistake correction (H = 198,19; p < 0,001). The results obtained in the two stages of the research provide valuable insights regarding the methods of short ski turns. The hierarchical classification of the most important method-ical corrective exercises obtained from ski teachers and professionals with different levels of education and expertise yields accurate and precise data about corrective methodical exercises in the process of studying short ski turn. Any further research regarding the same object should evaluate the designed expert model of the most important methodical exer-cises, as well as their hierarchical classification, across different groups of participants.

scholarly journals Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

2020 ◽  
Vol 12 (21) ◽  
pp. 3482
Author(s):  
Evelyn Uuemaa ◽  
Sander Ahi ◽  
Bruno Montibeller ◽  
Merle Muru ◽  
Alexander Kmoch
Keyword(s):  
Land Cover ◽  
Reference Model ◽  
Digital Elevation Models ◽  
Systematic Evaluation ◽  
Slope Aspect ◽  
Research Fields ◽  
Digital Elevation ◽  
Geographic Regions ◽  
Vertical Accuracy ◽  
Dem Accuracy

Freely available global digital elevation models (DEMs) are important inputs for many research fields and applications. During the last decade, several global DEMs have been released based on satellite data. ASTER and SRTM are the most widely used DEMs, but the more recently released, AW3D30, TanDEM-X and MERIT, are being increasingly used. Many researchers have studied the quality of these DEM products in recent years. However, there has been no comprehensive and systematic evaluation of their quality over areas with variable topography and land cover conditions. To provide this comparison, we examined the accuracy of six freely available global DEMs (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM) in four geographic regions with different topographic and land use conditions. We used local high-precision elevation models (Light Detection and Ranging (LiDAR), Pleiades-1A) as reference models and all global models were resampled to reference model resolution (1m). In total, 608 million 1x1 m pixels were analyzed. To estimate the accuracy, we generated error rasters by subtracting each reference model from the corresponding global DEM and calculated descriptive statistics for this difference (e.g., median, mean, root-mean-square error (RMSE)). We also assessed the vertical accuracy as a function of the slope, slope aspect, and land cover. We found that slope had the strongest effect on DEM accuracy, with no relationship for slope aspect. The AW3D30 was the most robust and had the most stable performance in most of the tests and is therefore the best choice for an analysis of multiple geographic regions. SRTM and NASADEM also performed well where available, whereas NASADEM, as a successor of SRTM, showed only slight improvement in comparison to SRTM. MERIT and TanDEM-X also performed well despite their lower spatial resolution.

scholarly journals EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

2016 ◽  
Vol III-2 ◽  
pp. 91-98 ◽  
Author(s):  
F. F. Asal
Keyword(s):  
Point Cloud ◽  
Digital Elevation Models ◽  
Statistical Characteristics ◽  
Lidar Data ◽  
High Point ◽  
Digital Elevation ◽  
Data Density ◽  
Standard Deviations ◽  
Dem Accuracy ◽  
Data Reductions

With continuous developments in LiDAR technologies high point cloud densities have been attainable but accompanied by challenges for processing big volumes of data. Reductions in high point cloud densities are expected to lower data acquisition and data processing costs; however this could affect the characteristics of the generated Digital Elevation Models (DEMs). This research aimed to evaluate the effects of reductions in airborne LiDAR point cloud data densities on the visual and statistical characteristics of the generated DEMs. DEMs have been created from a dataset which constitutes last returns of raw LiDAR data that was acquired at bare lands for Gilmer County, USA between March and April 2004, where qualitative and quantitative testing analyses have been performed. Visual analysis has shown that the DEM can withstand a considerable degree of quality with reduced densities down to 0.128&thinsp;pts/m<sup>2</sup> (47&thinsp;% of the data remaining), however degradations in the DEM visual characteristics appeared in coarser tones and rougher textures have occurred with more reductions. Additionally, the statistical analysis has indicated that the standard deviations of the DEM elevations have decreased by only 22&thinsp;% of the total decrease with data density reductions down to 0.101&thinsp;pts/m<sup>2</sup> (37&thinsp;% of the data remaining) while greater rate of decreasing in the standard deviations has occurred with more reductions referring to greater rate of surface smoothing and elevation approximating. Furthermore, the accuracy analysis testing has given that the DEM accuracy has degraded by only 4.83&thinsp;% of the total degradations with data density reductions down to 0.128&thinsp;pts/m<sup>2</sup>, however great deteriorations in the DEM accuracy have occurred with more data reductions. Finally, it is recommended that LiDAR data can withstand point density reductions down to 0.128&thinsp;pts/m<sup>2</sup> (about 50&thinsp;% of the data) without big deteriorations in the visual and statistical characteristics of the generated DEMs.

scholarly journals Classification of Pap smear images for Cervical cancer using Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 2801-2807
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Convolutional Neural Network ◽  
Pap Smear ◽  
Severe Dysplasia ◽  
Normal Cells ◽  
Second Stage ◽  
Two Stages ◽  
Cancerous Cells

Classification of Pap smear images for cervical cancer consists of two types namely, normal and abnormal cancerous cells. The dataset involves 7 sets of classes of cancerous images which have 3 sets of normal cancerous images and 4 sets of abnormal cancerous images. The proposed work performs two stages of classification. The first stage of the work is classifying the data as normal or abnormal cancerous cells. In the second stage of the work, the class of the cancer as normal columnar, normal intermediate, normal superficial, light dysplasia, moderate dysplasia, severe dysplasia and carcinoma_in_situ are classified. The proposed work gives good results for classifying images for 3 sets of classes and 4 sets of classes for normal cells and is able to classify and detect normal and abnormal cell accurately.

Sign in / Sign up

Export Citation Format

Share Document