Object-based contextual image classification built on image segmentation

By Thomas Blaschke. 2003

125 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Guisong Xia
6. Gui-Song Xia
7. Yanfei Zhong
8. Xiang Bai

In Media Technology

1. Liangpei Zhang
2. Gong Cheng
3. Junwei Han
4. Xiaoqiang Lu
5. Guisong Xia
6. Gui-Song Xia
7. Yanfei Zhong
8. Lei Guo

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “the task of remote sensing image analysis for decades [70-80].”
• Automated classification of landform elements using object-based image analysis (2006, methodology): “In this paper, we have demonstrated that a multiscale image segmentation/ object relationship modeling methodology (MSS/ORM, cf. Burnett and Blaschke, 2003) can also be efficiently used for geomorphometry and terrain classification.”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, background): “This kind of approaches then has dominated the analysis of highresolution remote sensing images for decades [47]–[53].”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “, 2012; Stumpf and Kerle, 2011), land cover and land use mapping (Baker et al., 2013; Benz et al., 2004; Blaschke, 2003; Blaschke et al., 2011; Blaschke et al., 2008; Contreras et al., 2015; D'Oleire-Oltmanns et al., 2014; De Pinho et al., 2012; Doleire-Oltmanns et al., 2013; Drăguţ and Blaschke, 20”
• A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images (2006, background): “Moreover, other aspects considered in this analysis are: 1) that objects at the same level are logically related to each other and 2) that each object at a generic level is hierarchically related to those at the higher and lower levels [7], [8], [13], [14].”
• An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery (2013, methodology): “Different spectralspatial feature extraction and classification methods were implemented, including differential MPs (DMPs) [12], graylevel co-occurrence matrix (GLCM), pixel shape index (PSI) [13], object-based classification using the fractal net evolution approach [14], and the multiscale mean-sh”
• Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification (2010, background): “It is critical in object-based classification since it is directly related to the size of resultant objects, influencing the spatial scale of the image analysis, which consequently impacts classification accuracy (Blaschke, 2003; Kim et al., 2008; Meinel and Neubert, 2004; Wang et al., 2004).”
• ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data (2010, methodology): “The segmentation process reduces the within-class spectral variation of VHR imagery, and can increase the classification and statistical accuracy if conducted at an appropriate scale (Blaschke 2003, Addink et al .”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “Subsequently, a series of approaches to analyze remote remote sensing images at object level has dominated remote sensing image analysis for the last two decades [33]–[36].”
• Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues (2009, background): “Blaschke (2003) suggests estimation of the appropriate size of image objects (i.e., optimal segmentation scale) is a critical, but challenging, issue in GEOBIA.”