Object-based target search using remotely sensed data: A case study in detecting invasive exotic Australian Pine in south Florida

Xie, Zhixiao; Roberts, C. Neil; Johnson, Brian Alan

doi:10.1016/j.isprsjprs.2008.04.003

Cited by 42 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Object-based results look smoother and show an overall better matching to the land cover types expected from visual image interpretation. The salt-and-pepper effect was clearly alleviated by object-based classification compared to the pixel-based results; this shows consistency with most research comparing the two approaches (Blaschke, 2010;Lillesand, Kiefer, & Chipman, 2014;Xie, Roberts, & Johnson, 2008).…”

Section: Resultssupporting

confidence: 80%

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

Pande-Chhetri

Abd-Elrahman

Liu

et al. 2017

European Journal of Remote Sensing

108

View full text Add to dashboard Cite

The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance. ARTICLE HISTORY

show abstract

Section: Resultssupporting

confidence: 80%

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

Pande-Chhetri

Abd-Elrahman

Liu

et al. 2017

European Journal of Remote Sensing

108

View full text Add to dashboard Cite

show abstract

“…For instance, Yu et al (2006) created a comprehensive vegetation inventory for a study area in Northern California and could empirically demonstrate that the OBIA approach overcame the problem of salt-and-pepper effects found in classification results from traditional per pixel approaches. (Xie et al, 2008) used an object based geographic image retrieval approach for detecting invasive, exotic Australian Pine in South Florida, USA. Dorren et al (2003) as well as Heyman et al (2003) favoured an OBIA approach to discriminate broadscale forest cover types, and in a subsequent study Maier et al (2008) incorporated very detailed information from LiDAR-derived canopy surface models.…”

Section: Obia Studiesmentioning

confidence: 99%

Object based image analysis for remote sensing

Blaschke

2010

ISPRS Journal of Photogrammetry and Remote Sensing

3,695

2,210

View full text Add to dashboard Cite

a b s t r a c tRemote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA -or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

show abstract

“…Multi-scale/multi-level GEOBIA (MS-GEOBIA) approaches, which incorporate multiple segmentations of an image at different "scale" levels for LULC classification (i.e., different average segment sizes), often outperform single-scale/single-level GEOBIA (SS-GEOBIA) approaches when LULC features of interest differ in size and/or texture [8][9][10][11], as even a very accurate single-level segmentation will likely split some LULC features into multiple segments (i.e., oversegment) and/or group them together with other neighboring LULC features in a single segment (i.e., undersegment). Many MS-GEOBIA studies involve classifying different LULC types at each segmentation level [8,[12][13][14][15], while few have compared SS-GEOBIA and MS-GEOBIA approaches for classifying a single LULC type of interest [16]. Residential areas vary in terms of both size (area of residential development) and texture (e.g., building sizes and building densities which vary by development), so a MS-GEOBIA approach may be more appropriate than SS-GEOBIA for mapping them.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery

Johnson

Bragais

Endo

et al. 2015

IJGI

Self Cite

101

View full text Add to dashboard Cite

Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA) methods are often unable to obtain an accurate segmentation and classification of all land use/land cover (LULC) types in an image. However, there have been few comparisons between SS-GEOBIA and MS-GEOBIA approaches for the purpose of mapping a specific LULC type, so it is not well understood which is more appropriate for this task. In addition, there are few methods for automating the selection of segmentation parameters for MS-GEOBIA, while manual selection (i.e., trial-and-error approach) of parameters can be quite challenging and time-consuming. In this study, we examined SS-GEOBIA and MS-GEOBIA approaches for extracting residential areas in Landsat 8 imagery, and compared naï ve and parameter-optimized segmentation approaches to assess whether unsupervised segmentation parameter optimization (USPO) could improve the extraction of residential areas. Our main findings were: (i) the MS-GEOBIA approaches achieved higher classification accuracies than the SS-GEOBIA approach, and (ii) USPO

show abstract

Object-based target search using remotely sensed data: A case study in detecting invasive exotic Australian Pine in south Florida

Cited by 42 publications

References 37 publications

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

Object based image analysis for remote sensing

Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery

Contact Info

Product

Resources

About