scholarly journals Mapping Population Distribution from High Resolution Remotely Sensed Imagery in a Data Poor Setting

2018 ◽  
Vol 10 (9) ◽  
pp. 1409 ◽  
Author(s):  
Sophie Mossoux ◽  
Matthieu Kervyn ◽  
Hamid Soulé ◽  
Frank Canters
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Population Distribution ◽  
Remotely Sensed ◽  
Great Promise ◽  
Remotely Sensed Imagery ◽  
Combining Data ◽  
Neighborhood Level

Accurate mapping of population distribution is essential for policy-making, urban planning, administration, and risk management in hazardous areas. In some countries, however, population data is not collected on a regular basis and is rarely available at a high spatial resolution. In this study, we proposed an approach to estimate the absolute number of inhabitants at the neighborhood level, combining data obtained through field work with high resolution remote sensing. The approach was tested on Ngazidja Island (Union of the Comoros). A detailed survey of neighborhoods at the level of individual dwellings, showed that the average number of inhabitants per dwelling was significantly different between buildings characterized by a different roof type. Firstly, high spatial resolution remotely sensed imagery was used to define the location of individual buildings, and second to determine the roof type for each building, using an object-based classification approach. Knowing the location of individual houses and their roof type, the number of inhabitants was estimated at the neighborhood level using the data on house occupancy of the field survey. To correct for misclassification bias in roof type discrimination, an inverse calibration approach was applied. To assess the impact of variations in average dwelling occupancy between neighborhoods on model outcome, a measure of the degree of confidence of population estimates was calculated. Validation using the leave-one-out approach showed low model bias, and a relative error at the neighborhood level of 17%. With the increasing availability of high resolution remotely sensed data, population estimation methods combining data from field surveys with remote sensing, as proposed in this study, hold great promise for systematic mapping of population distribution in areas where reliable census data are not available on a regular basis.

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

Use of Remotely Sensed Imagery in Cyber Warfare and Cyber Counterterrorism

2011 ◽  
pp. 298-305
Author(s):  
Gang Gong ◽  
Mark R. Leipnik
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remotely Sensed ◽  
Aerial Photographs ◽  
The Internet ◽  
Cyber Warfare ◽  
Future Trends ◽  
Remotely Sensed Imagery ◽  
General Terms ◽  
High Resolution Imagery

Remote sensing refers to the acquisition of information at a distance. More specifically, it has come to mean using aerial photographs or sensors on satellites to gather data about features on the surface of the earth. In this article, remote sensing and related concepts are defined and the methods used in gathering and processing remotely sensed imagery are discussed. The evolution of remote sensing, generic applications and major sources of remotely sensed imagery and programs used in processing and analyzing remotely sensed imagery are presented. Then the application of remote sensing in warfare and counterterrorism is discussed in general terms with a number of specific examples of successes and failures in this particular area. Next, the potential for misuse of the increasing amount of high resolution imagery available over the Internet is discussed along with prudent countermeasures to potential abuses of this data. Finally, future trends with respect to this rapidly evolving technology are included.

Study on Remote Sensing Images of De-Blurring Based on Dictionary Learning

2015 ◽  
Vol 738-739 ◽  
pp. 217-222
Author(s):  
Yan Jia ◽  
Zhen Tao Qin ◽  
Bang Xin Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Dictionary Learning ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Research Field ◽  
Projection Algorithm ◽  
Remote Sensing Images ◽  
Novel Algorithm

De-blurring the high resolution remote sensing images is an important issue in the relative research field of remote sensing. In this paper a novel algorithm of de-blurring the high resolution remote sensing images is proposed based on sparse representation. The high spatial resolution remote sensing images can be de-blurred by gradient projection algorithm, and keep the useful information of the image. The experimental results of the remote sensing images obtained by “the first satellite of high resolution” show that the algorithm can de-blur the image more effectively and improve the PSNR, this method has better performance than other dictionary learning algorithm.

scholarly journals RESEARCH ON HORIZONTAL ACCURACY METHOD OF HIGH SPATIAL RESOLUTION REMOTELY SENSED ORTHOPHOTO IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 2005-2007
Author(s):  
Y. M. Xu ◽  
J. X. Zhang ◽  
F. Yu ◽  
S. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Field Measurement ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
High Accuracy ◽  
New Method ◽  
Orthophoto Images

At present, in the inspection and acceptance of high spatial resolution remotly sensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points’ source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

A Boundary Parallel-Like Index for High-Resolution Remotely Sensed Imagery Classification

2019 ◽  
Vol 33 (04) ◽  
pp. 1954014 ◽  
Author(s):  
Weiwei Jiang ◽  
Henglin Xiao ◽  
Zhan Zhao ◽  
Jianguo Zhou
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
Shape Features ◽  
Remote Sensing Imagery ◽  
Remotely Sensed Imagery ◽  
Remote Sensing Image Classification ◽  
Discriminating Ability

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

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