Classification of Iowa wetlands using an airborne hyperspectral image: a comparison of the spectral angle mapper classifier and an object-oriented approach

2005 ◽  
Vol 31 (2) ◽  
pp. 167-174 ◽  
Author(s):  
James Harken ◽  
Ramanathan Sugumaran
Keyword(s):  
Hyperspectral Image ◽  
Object Oriented ◽  
Spectral Angle Mapper ◽  
Object Oriented Approach ◽  
Oriented Approach ◽  
Spectral Angle

scholarly journals Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1136 ◽  
Author(s):  
Muhammad Ahmad ◽  
Asad Khan ◽  
Adil Mehmood Khan ◽  
Manuel Mazzara ◽  
Salvatore Distefano ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Sample Selection ◽  
Spectral Angle Mapper ◽  
Classifier Design ◽  
Heterogeneous Samples ◽  
Marginal Probability Distribution ◽  
Benchmark Datasets ◽  
Manual Selection ◽  
Spectral Angle

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

Object-oriented approach

IEE Review ◽  
1990 ◽  
Vol 36 (9) ◽  
pp. 338
Author(s):  
Stephen Wilson
Keyword(s):  
Object Oriented ◽  
Object Oriented Approach ◽  
Oriented Approach
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