Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongchao; Ma, Xiaowei

doi:10.1016/j.isprsjprs.2014.12.026

Cited by 182 publications

(126 citation statements)

References 56 publications

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“…These findings are in line with recent publication of Ma et al (2015) who also used UAS imagery. The lower overall accuracy values of the manually delineated tree crown scale (despite a lower mean area value than the OBIA 60 scale objects) can be further explained by the fact that a major part of delineated individuals in the reference dataset is actually clump of trees.…”

Section: Scale Of Analysissupporting

confidence: 92%

“…This threshold is relatively restrictive regarding to other comparable studies (60 % for Ma et al (2015) and even 50 % Stumpf and Kerle (2011)). This choice was justified because of the relatively high similarity between classes compared to these two studies (land cover classes for Ma et al (2015) and Blandslide^vs Bother objects^for Stumpf and Kerle (2011)). …”

Section: Generation Of Sample Databasementioning

confidence: 85%

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Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

204

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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Section: Scale Of Analysissupporting

confidence: 92%

Section: Generation Of Sample Databasementioning

confidence: 85%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

204

179

View full text Add to dashboard Cite

show abstract

“…An object-based image analysis (OBIA) is used for UAV image classification because the geometrical and contextual features can be incorporated into the classification [29,30]. The approach segments the UAV image into ecological patches, is combined with a decision tree model at the object level, and is able to improve the classification accuracy.…”

Section: Reference Sip Extraction From the Uav Datamentioning

confidence: 99%

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Xia

Zhao

et al. 2017

Remote Sensing

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Abstract:Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R 2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R 2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

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“…In [34] they said that "GEOBIA is a systematic framework for geographic object identification, which combines pixels with the same semantic information into an object, thereby generating an integrated geographic object." GEOBIA is a newly developed area of Geographic Information Science and remote sensing in which the automatic segmentation of images into objects of similar spectral, temporal and spatial characteristics is carried out [35].…”

Section: Geographic Object Based Image Analysismentioning

confidence: 99%

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Minaei

Kainz

2016

IJGI

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Abstract:The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future.

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Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

Cited by 182 publications

References 56 publications

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

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