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Object-based classification of multi-sensor optical imagery to generate terrain surface roughness information for input to wind risk simulation

Alan Forghani, Bob Cechet, Krishna Nadimpalli
2007 2007 IEEE International Geoscience and Remote Sensing Symposium  
It was necessary to investigate the applicability of multi-sensor approaches to generate a regional/national terrain surface roughness map based on the Australian/New Zealand wind loading standard (AS/  ...  MODIS, Landsat, and IKONOS imagery were acquired (from 12 September -26 November 2002) covering a significant portion of the New South Wales, Australia.  ...  ACKNOWLEDGEMENTS The project was undertaken to support Geoscience Australia Risk Research Group's Wind Risk Assessment activity while the first author was on secondment to the above group.  ... 
doi:10.1109/igarss.2007.4423498 dblp:conf/igarss/ForghaniCN07 fatcat:2neeek3zxbcmroixed4sjkbejy

Reunderstanding Geomorphological Features in Chang'e-5 Sampling Region Based on Multiscale Roughness Model

Wei Cao, Zhiguo Meng, Xuegang Dong, Jietao Lei, Minggang Xie, Juqing Yang, Zhanchuan Cai
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Based on these signatures, the hectometer-and kilometer-scale roughness are calculated and mapped.  ...  As a typical surface analyzing method, multi-scale surface roughness has been used in planetary studies for a long time.  ...  Based on the roughness textural changes on the maps, we interpret that these changes can provide better understandings: First, the multi-scale roughness textural changes can reveal new relationships between  ... 
doi:10.1109/jstars.2021.3110731 fatcat:ave74v7d2bg2nogflu7wqpkzbm

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

Jochen Veitinger, Ross Stuart Purves, Betty Sovilla
2016 Natural Hazards and Earth System Sciences  
By introducing a multi-scale roughness parameter, fine-scale topography and its attenuation under snow influence is captured.  ...  Therefore, a new algorithm for automated identification of potential avalanche release areas was developed.  ...  Schernthanner and one anonymous referee  ... 
doi:10.5194/nhess-16-2211-2016 fatcat:obndacz7djfitdwikouv6lm6ua

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

J. Veitinger, R. S. Purves, B. Sovilla
2015 NHESSD  
By introducing a multi-scale roughness parameter, fine-scale topography and its attenuation under snow influence is captured.  ...  Therefore, a new algorithm for automated identification of potential avalanche release areas was developed.  ...  Schernthanner and one anonymous referee  ... 
doi:10.5194/nhessd-3-6569-2015 fatcat:3touxm45anes3iof54txxlb4wu

APPLICATION OF DEEP LEARNING IN GLOBELAND30-2010 PRODUCT REFINEMENT

T. Liu, X. Chen
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The explosive high-resolution satellite images and remarkable performance of Deep Learning on image classification provide a new opportunity to refine GlobeLand30.  ...  The result shows that the fine-tuning from first layer of Inception V3 using rough large sample set is the best strategy.  ...  Those construction methods based on data fusion perform well for specific land cover types at regional scale.  ... 
doi:10.5194/isprs-archives-xlii-3-1111-2018 fatcat:xkymnpbom5as7nk44qoeauoaqu

Multiscale Modelization of Multilayered Bi-Dimensional Soils

I. Hosni, L. Bennaceur Farah, N. Saber, R Bennaceur
2018 Zenodo  
In this study, surfaces are considered as band-limited fractal random processes corresponding to a superposition of a finite number of one-dimensional Gaussian process each one having a spatial scale.  ...  Accurate modeling of the above processes depends on the ability to provide a proper spatial characterization of soil moisture.  ...  The method is based on a synergistic microwave/optical model to simulate the radar backscatter from a green surface based on WCM model [24] and the two-dimensional SPM model.  ... 
doi:10.5281/zenodo.2021588 fatcat:wni6xmf65vclhkuekyx5qcr2ie

Review and prioritization of investment projects in the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach

Vahid Saeid Nahaei, Mohammadali Habibizad Novin, Mahdi Assadi Khaligh
2021 International Journal of Innovation in Management, Economics and Social Sciences  
This research offers new methods for projects and their diversity according to Rough Sets technique.  ...  of experts and the weight and priority of the projects were obtained using the Rough Sets Theory.  ...  Thus, a study proposes a methodology based on weighted multi granulation fuzzy rough sets (MGFRSs) over two universes to perform risk evaluation for PPP WTE incineration plant projects.  ... 
doi:10.52547/ijimes.1.3.46 fatcat:3c7nrp6m75aj7aj6u5g3b7jsqm

Roughness exponents to calculate multi-affine fractal exponents

A. Castro e Silva, J.G. Moreira
1997 Physica A: Statistical Mechanics and its Applications  
We propose a new method to determine the multi-affine fractal exponents based on a generalization of the roughness concept.  ...  Also in the present method, scaling is observed using profiles with a number of points more than one order of magnitude smaller.  ...  In conclusion, we introduce a method to determine the multi-affine fractal exponents Hq which is based on a generalization of the roughness exponent (Eqs. (6) and (7) ).  ... 
doi:10.1016/s0378-4371(96)00357-3 fatcat:himp6c3kkfgq7pkifqg7hhiyry

Preface "Observing and modeling the catchment scale water cycle"

X. Li, X. W. Li, K. Roth, M. Menenti, W. Wagner
2011 Hydrology and Earth System Sciences  
TSEBPS is based on the theory of the classical two-layer energy balance model and on a set of new formulations derived from assumptions of energy balance at limiting cases.  ...  As a methodologically new aspect the authors use terrain-corrected snow cover remote sensing products in the model.  ... 
doi:10.5194/hess-15-597-2011 fatcat:bd2u42trpba3jnh6hjbnlay2q4

Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band

Sandy Peischl, Jeffrey P. Walker, Dongryeol Ryu, Yann H. Kerr, Rocco Panciera, Christoph Rudiger
2012 IEEE Transactions on Geoscience and Remote Sensing  
The model set up was based on two types of input parameters: i) default model 222 parameters as a function of the land cover class and ii) ground truth information collected at the 223 focus farms.  ...  field experiments (SMOSREX [17], MELBEX [18] and EuroSTARRS [19]), 78 with the modeling of incidence angle relationships based on only a subset of the possible land 79 cover types.  ... 
doi:10.1109/tgrs.2011.2174644 fatcat:stnde4bfdvdo7kahzmznufjx4q

A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

C. Zhang, X. Pan, S. Q. Zhang, H. P. Li, P. M. Atkinson
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
<br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness  ...  The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom.  ...  ACKNOWLEDGEMENTS This research was supported by PhD studentship "Deep Learning in massive area, multi-scale resolution remotely sensed imagery" (NO.  ... 
doi:10.5194/isprs-archives-xlii-2-w7-1451-2017 fatcat:w4pg3aco6faojpark43edswx64

Pupil Localization for Multi-View Eyeballs by ASM and Eye Gray Distribution

Xianmei Wang, Xiujie Zhao, Liying Jia, Ping Yang
2011 Procedia Engineering  
A set of experiments on different eyeball positions are also presented.  ...  This paper presents a new approach for pupil localization in multi-view eyeballs under ordinary light conditions. There are two key steps.  ...  Multi-scale ASM for key facial feature points Multi-scale ASM is built on pyramid analysis of image and includes the following two steps. First, search the target in a rough image.  ... 
doi:10.1016/j.proeng.2011.08.563 fatcat:76qbwofjz5akzm4ztloijosbnq

Incremental Knowledge Acquisition for WSD: A Rough Set and IL based Method

Xu Huang, Xiulan Hao, Qing Shen, Bin Shao
2015 EAI Endorsed Transactions on Scalable Information Systems  
To acquire knowledge about Chinese multi-sense verbs, we introduce an incremental machine learning method which combines rough set method and instance based learning.  ...  First, context of a multi-sense verb is extracted into a table; its sense is annotated by a skilled human and stored in the same table.  ...  In our work, we integrate rough set based method with IL to acquire knowledge for WSD.  ... 
doi:10.4108/sis.2.5.e3 fatcat:73qnoasqdbfolmfhp24q43fbga

Special issue on intelligent decision support systems based on soft computing and their applications in real-world problems

Enrique Herrera-Viedma, Francisco Chiclana, Yucheng Dong, Francisco Javier Cabrerizo
2018 Applied Soft Computing  
Intelligent decision support systems based on soft computing are of  ...  In recent years, intelligent decision support systems based on soft computing have attracted the attention of both, academic researchers and practitioners in a wide range of disparate areas from computing  ...  Cheruku et al. in RST-BatMiner: A Fuzzy Rule Miner Integrating Rough Set Feature Selection and Bat Optimization for Detection of Diabetes Disease propose a decision support system based on rough set theory  ... 
doi:10.1016/j.asoc.2018.04.054 fatcat:ma5kdazxvvgrfc2snma6p4lwbu

Double-quantitative γ^∗-fuzzy coverings approximation operators [article]

Guangming Lang
2016 arXiv   pre-print
In digital-based information boom, the fuzzy covering rough set model is an important mathematical tool for artificial intelligence, and how to build the bridge between the fuzzy covering rough set theory  ...  and Pawlak's model is becoming a hot research topic.  ...  rough fuzzy sets, decision-theoretic rough sets, doublequantitative rough sets, multi-granulation rough sets, and so on.  ... 
arXiv:1611.08103v1 fatcat:g2g752oinvd6zdydetisa3t5o4
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