Multiple stratified sampling strategy for assessing the big remote sensing products

2015 ◽  
Author(s):  
Huan Xie ◽  
Xiaohua Tong ◽  
Wen Meng ◽  
Fang Wang ◽  
Xiong Xu
Keyword(s):  
Remote Sensing ◽  
Sampling Strategy ◽  
Stratified Sampling

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

scholarly journals Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 174 ◽  
Author(s):  
Han Liu ◽  
Jun Li ◽  
Lin He ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Sampling Strategy ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Spatial Dependency ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

Geographically Stratified Importance Sampling for the Calibration of Aggregated Destination Choice Models for Trip Distribution

2005 ◽  
Vol 1935 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Min-Tang Li ◽  
Lee-Fang Chow ◽  
Fang Zhao ◽  
Shi-Chiang Li
Keyword(s):  
Importance Sampling ◽  
Random Sampling ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Choice Models ◽  
Stratified Sampling ◽  
Destination Choice ◽  
Selection Probabilities ◽  
Choice Probabilities ◽  
Choice Set

A key feature in estimating and applying destination choice models with aggregate alternatives is to sample a set of nonchosen traffic analysis zones (TAZs), plus the one a trip maker chose, to construct a destination choice set. Computational complexity is reduced because the choice set would be too large if all study area TAZs were included in the calibration. Commonly, two types of sampling strategies are applied to draw subsets of alternatives from the universal choice set. The first, and simplest, approach is to select randomly a subset of nonchosen alternatives with uniform selection probabilities and then add the chosen alternative if it is not otherwise included. The approach, however, is not an efficient sampling scheme because most alternatives for a given trip maker may have small choice probabilities. The second approach, stratified importance sampling, draws samples with unequal selection probabilities determined on the basis of preliminary estimates of choice probabilities for every alternative in the universal choice set. The stratified sampling method assigns different selection probabilities to alternatives in different strata. Simple random sampling is applied to draw alternatives in each stratum. However, it is unclear how to divide the study area so that destination TAZs may be sampled effectively. The process of and findings from implementing a stratified sampling strategy in selecting alternative TAZs for calibrating aggregate destination choice models in a geographic information system (GIS) environment are described. In this stratified sampling analysis, stratum regions varied by spatial location and employment size in the adjacent area were defined for each study area TAZ. The sampling strategy is more effective than simple random sampling in regard to maximum log likelihood and goodness-of-fit values.

scholarly journals The Six Cities Project: developing a methodology of surveying densely populated areas using social science assisted and diachronic remote sensing based classification of habitation

2003 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Thomas K. Park ◽  
Mamadou Baro
Keyword(s):  
Remote Sensing ◽  
National Science Foundation ◽  
Survey Methodology ◽  
Statistical Evaluation ◽  
Sampling Strategy ◽  
Household Labor ◽  
Livelihood Strategies ◽  
Term Basis

This paper provides a statistical evaluation of the methodology of the NSF funded Six Cities Project. The project develops a methodology for surveying densely inhabited areas by processing diachronic remote sensing imagery to create habitation strata or urban classes. These classes become part of a sampling strategy which gives every pixel associated with habitation a specified chance of selection and then draws a representative sample of pixels. These pixels become center points for household surveys which can study a variety of issues including health, environment, livelihood strategies, demographics and household labor, expenditures and income. The methodology lends itself to GIS construction and the generation of data that can be easily compared and can be of maximal use to municipalities, governments, scholars and NGOs. It also provides a long term basis for inexpensive surveys that can have a high claim to reliability and representativity.Key words: remote sensing, urbanism, survey methodology, National Science Foundation, health, environment, livelihood strategies, demographic, household labor, expenditures, income, Africa, Middle East, Morocco, Senegal, Mali, Niger, Tanzania, Botswana, Marrakech, Dakar, Bamako, Niamey, Dodoma,  Gaborone. 

scholarly journals Accelerating Stratified Sampling SGD by Reconstructing Strata

2020 ◽  
Author(s):  
Weijie Liu ◽  
Hui Qian ◽  
Chao Zhang ◽  
Zebang Shen ◽  
Jiahao Xie ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Convergence Rate ◽  
Numerical Experiments ◽  
High Probability ◽  
State Of The Art ◽  
Sampling Strategy ◽  
The State ◽  
Stratified Sampling

In this paper, a novel stratified sampling strategy is designed to accelerate the mini-batch SGD. We derive a new iteration-dependent surrogate which bound the stochastic variance from above. To keep the strata minimizing this surrogate with high probability, a stochastic stratifying algorithm is adopted in an adaptive manner, that is, in each iteration, strata are reconstructed only if an easily verifiable condition is met. Based on this novel sampling strategy, we propose an accelerated mini-batch SGD algorithm named SGD-RS. Our theoretical analysis shows that the convergence rate of SGD-RS is superior to the state-of-the-art. Numerical experiments corroborate our theory and demonstrate that SGD-RS achieves at least 3.48-times speed-ups compared to vanilla minibatch SGD.

Application of Spatial Sampling to Remote Sensing Monitoring of Forest Cover Area

2012 ◽  
Vol 610-613 ◽  
pp. 3732-3737 ◽  
Author(s):  
Ji Ping Zhang ◽  
Lin Bo Zhang ◽  
Bin Gong
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Random Sampling ◽  
Vegetation Index ◽  
Forest Cover ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Spatial Sampling ◽  
Remote Sensing Monitoring ◽  
Study Results

This study combines the sampling technique, geographic information system and remote sensing technique to conduct a sampling survey on forest cover area of Jinggangshan National Nature Reserve in China on the basis of TM remote sensing image. The spatial simple random sampling, spatial stratified sampling and sandwich sampling model are respectively utilized to establish the sampling design. For the spatial simple random sampling model, the spatial autocorrelation analysis method is adopted to determine the spatial autocorrelation coefficient through calculating Moran's I index, while in the spatial stratified sampling and sandwich sampling model, the yearly maximum NDVI (Normalized Difference Vegetation Index) is utilized to conduct the spatial stratification. Through comparison of the sampling accuracy of three sampling models, a higher precision and more reasonable sampling method and sampling model is provided for remote sensing monitoring of forest cover area. The study results show that: sandwich sampling model is featured as the highest sampling accuracy, followed by the spatial stratified sampling and simple random sampling. Under the requirement of same precision, sandwich spatial sampling model can reduce quantity of the sampling points, and create all kinds of report units according to demands of different spatial area, so it is featured as the better suitability.

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