Land use studies in drylands: an evaluation of object‐oriented classification of very high resolution panchromatic imagery

Elmqvist, Bodil; Ardö, Jonas; Olsson, Lennart

doi:10.1080/01431160802238419

Cited by 23 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process is similar to the spectral difference (SD) segmentation process that grows neighboring image objects according to their mean image layer intensity values and merges neighboring objects based on user-defined spectral difference criteria [40]. Although SD segmentation is designed to reshape existing image objects, when it deals with large image datasets such as a subset of our study area, the computational time is much greater than that of the MRSRG method.…”

Section: Resultsmentioning

confidence: 99%

“…The segmentation algorithm first identifies a set of starting points (seed points) of a segmentation process and then joins contiguous pixels to the seed points if they fulfill the homogeneity criteria until certain thresholds are reached [40]. "Scale" is one of the important criteria in segmentation process.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Three criteria are defined in the Definiens software (formerly known as eCognition software) to constrain the pixel growing algorithm, namely color, shape and scale, to control smoothness and compactness of image objects [41]. Smoothness is defined as the ratio of an object's perimeter to the perimeter of this object's boundaries that run parallel to the image borders [42]; compactness is the ratio of an object's perimeter to the square root of the number of pixels within that image object [40].…”

Section: Image Segmentationmentioning

confidence: 99%

“…Expert knowledge in spatial and spectral features was applied to identify roads. We used the "multi-resolution segmentation region grow" (MRSRG) algorithm in three continuous steps to reshape and merge segmented image objects based on the MR segmentation criteria [40] (Figure 6). As to the parameter settings, the PCA1 band was selected as the only active layer for image object growth.…”

Section: Non-vegetation Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Shao

2014

Remote Sensing

View full text Add to dashboard Cite

There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km 2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in OPEN ACCESSRemote Sens. 2014, 6 11373 image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Section: Non-vegetation Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Shao

2014

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These objects are defined to maximize between-object variability and minimize within-object variability for user-chosen inputs. The segmentation algorithm first identifies a set of starting points (seed points) of a segmentation process and then joins contiguous pixels to the seed points if they fulfill the homogeneity criteria until certain thresholds are reached (Elmqvist et al, 2008). Scale, Shape and Compactness are parameters for image segmentation.…”

Section: Object-based Ruleset Classification Systemmentioning

confidence: 99%

Object-Based Forest Cover Monitoring Using Gaofen-2 High Resolution Satellite Images

Chen

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Forest cover monitoring is an important part of forest management in local or regional area. The structure and tones of forest can be identified in high spatial remote sensing images. When forests cover change, the spectral characteristics of forests is also changed. In this paper a method on object-based forest cover monitoring with data transformation from time series of high resolution images is put forward. First the NDVI difference image and the composite of PC3,PC4, PC5 of the stacked 8 layers of time series of high resolution satellites are segmented into homogeneous objects. With development of the object-based ruleset classification system, the spatial extent of deforestation and afforestation can be identified over time across the landscape. Finally the change accuracy is achieved with reference data.

show abstract

Application of an Object-Oriented Method for Classification of VHR Satellite Images Using a Rule-Based Approach and Texture Measures

Lewiński

Bochenek

Turlej

2014

Land Use and Land Cover Mapping in Europe

View full text Add to dashboard Cite

Land use studies in drylands: an evaluation of object‐oriented classification of very high resolution panchromatic imagery

Cited by 23 publications

References 19 publications

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Object-Based Forest Cover Monitoring Using Gaofen-2 High Resolution Satellite Images

Application of an Object-Oriented Method for Classification of VHR Satellite Images Using a Rule-Based Approach and Texture Measures

Contact Info

Product

Resources

About