scholarly journals Value of feature reduction for crop differentiation using multi-temporal imagery, machine learning, and object-based image analysis

10.3990/2.378 ◽  
2016 ◽  
Author(s):  
J.K. Gilbertson ◽  
A. van Niekerk
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Feature Reduction ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Multi Temporal

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

2014 ◽  
Vol 150 ◽  
pp. 172-187 ◽  
Author(s):  
Chris M. Roelfsema ◽  
Mitchell Lyons ◽  
Eva M. Kovacs ◽  
Paul Maxwell ◽  
Megan I. Saunders ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Approach ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Multi Temporal ◽  
Seagrass Cover

scholarly journals Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping

2019 ◽  
Vol 11 (5) ◽  
pp. 503 ◽  
Author(s):  
Sachit Rajbhandari ◽  
Jagannath Aryal ◽  
Jon Osborn ◽  
Arko Lucieer ◽  
Robert Musk
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Forest Type ◽  
Rule Based ◽  
Object Based Image Analysis ◽  
Type Mapping ◽  
Object Based ◽  
Geographic Object

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.

Winter Wheat Mapping Using Landsat 8 Images and Geographic Object-Based Image Analysis

2017 ◽  
Vol 60 (3) ◽  
pp. 625-633
Author(s):  
Tengfei Su ◽  
Shengwei Zhang
Keyword(s):  
Image Analysis ◽  
Winter Wheat ◽  
Feature Reduction ◽  
Classification Model ◽  
Feature Analysis ◽  
Landsat 8 ◽  
Scale Selection ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

Abstract. Winter wheat is a major food source in many areas, so it is necessary to construct an effective approach for its monitoring based on satellite data. By taking advantage of geographic object-based image analysis (GEOBIA), a winter wheat classification framework was established. Two stages, which included scale selection and feature analysis, were incorporated into the new approach. The scale selection stage was implemented based on an unsupervised method, so human intervention for tuning the scale parameter of image segmentation can be largely saved. The feature analysis stage was performed on the basis of a random forest classification model, and in the experiment this step allowed for feature reduction, which was validated to be beneficial to the classification performance. Keywords: Feature analysis, GEOBIA, Scale selection, Winter wheat.

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