Accuracy Assessment Measures for Object Extraction from Remote Sensing Images

Cai, Liping A.; Shi, Wenzhong; Zhang, Miao; Hao, Ming

doi:10.3390/rs10020303

Cited by 50 publications

(31 citation statements)

References 34 publications

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“…Area-based accuracy measures (i.e. correctness, completeness and quality) (Cai et al, 2018), shape similarity (Lizarazo, 2014), and object matching (Cai et al, 2018) were considered for the accuracy assessment of features extracted by OBIA method.…”

Section: Figure 2 Flow Chart Showing Steps Of Methods Used In the Rementioning

confidence: 99%

“…Area-based accuracy measures are correctness (PAC), completeness (PAR) and quality (PAL) (Cai et al, 2018) used to evaluate OBIA which are dependent upon the correctly extracted area (AC), total extracted area (ADC) of segments and total reference area (ARC). These are calculated by the following methods:…”

Section: Figure 2 Flow Chart Showing Steps Of Methods Used In the Rementioning

confidence: 99%

“…Object Matching applies maximum overlap area algorithm to estimate coincidence degree (Oij) between evaluated objects and reference objects (Cai et al, 2018) as follow.…”

Section: = (10)mentioning

confidence: 99%

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Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

Budha¹,

Bhardwaj

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Locating landslides and determining its extent is deemed an important task in estimating loss and damage and carry out mitigation works. As landslides are recurring phenomena in the research site, Siwalik Hills of western Nepal, freely available Sentinel-2 satellite images were considered to delineate landslides. The method employed in this process was Object-Based Image Analysis carried out in eCognition software using multiresolution segmentation algorithm. Parameters taken for segmentation were a scale of 20, the shape of 0.3, and compactness of 0.5. When a threshold value of < 0.35 in NDVI was used to distinguish landslides from image objects, some non-landslide objects were also selected. These false positives were removed successively using the threshold values on different bands, band ratios, slope information, hillshade and geometrical properties of image objects. There were altogether 264 landslides detected in the study area with size ranging from 300 m2 to 1675 m2 and landslide density of approximately 2 per km2. The accuracy, when compared to reference inventory, showed correctness and completeness measuring 80.28% and 66.27% respectively. These results showed semi-automatic landslide extraction was successful and Sentinel-2 can be used for similar tasks in other areas of Siwalik.

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Section: Figure 2 Flow Chart Showing Steps Of Methods Used In the Rementioning

confidence: 99%

Section: Figure 2 Flow Chart Showing Steps Of Methods Used In the Rementioning

confidence: 99%

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Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

Budha¹,

Bhardwaj

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Object-based accuracy assessment is commonly not done-even within OBIA, as it is far more complex. Several measures have been proposed in the literature (e.g., [67,68]) that quantify the overlap of a classified and a reference object, e.g., the correctness, which is the ratio of the correctly mapped area compared to the total mapped area. Locational-existential methods assess whether an object (e.g., a deprived settlement) has been mapped at the location where the reference data indicate the existence of such a settlement.…”

Section: Uncertainties Caused By High Temporal Dynamics Of Deprived Amentioning

confidence: 99%

The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

Kuffer

Wang

Nagenborg

et al. 2018

IJGI

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The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1.1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1.1 indicator, when uncertainties and ethical considerations on data provision are well addressed.

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“…To validate the results the criteria Completeness, Correctness and Quality were used (Cai et al, 2018):…”

Section: Figure 3 Workflow Diagrammentioning

confidence: 99%

Intraseasonal Variability of Guano Stains in a Remotely Sensed Penguin Colony Using Uav and Satellite

Firla

Mustafa

Pfeifer

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Remote sensing of penguins gives a unique opportunity to observe ecosystem changes in the Antarctic and the Southern Ocean at a continent-wide scale. The extent of guano is the best proxy to the size of penguin populations but frequent cloud cover limits the number of available images. This study focuses on the correlation between guano coverage visible in aerial and satellite images and breeding pair numbers in the course of the breeding seasons 2016/17 and 2017/18 in a colony of Pygocelid penguins on Ardley Island (South Shetland Islands, Antarctica). Multitemporal UAV (Unmanned Aerial Vehicle) orthomosaics and high-resolution satellite images were collected of Ardley Island as well as data on breeding phenology, weather conditions and snow coverage. “Fresh” guano stains were classified using different methods of Geographical Object-based Image Analysis (GEOBIA) and differentiated from weathered guano stains. Analysis of this data shows that guano stains in an Antarctic Pygoscelid penguin colony undergo significant intraseasonal changes in extent, texture and spectral signature. Hence, the timing of image acquisition and the advance of snow melt during Antarctic spring matter when determining penguin populations and should be considered during the analysis. Our results show changes of up to 25&thinsp;% of the total guano covered surface due to individual weather events and changes up to 80&thinsp;% in the time between the peak of egg laying and the occurrence of the first crèche.</p>

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Accuracy Assessment Measures for Object Extraction from Remote Sensing Images

Cited by 50 publications

References 34 publications

Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

Intraseasonal Variability of Guano Stains in a Remotely Sensed Penguin Colony Using Uav and Satellite

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