Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task

1997 ◽  
Author(s):  
Palma N. Blonda ◽  
Andrea Baraldi ◽  
G. Bafunno ◽  
Giuseppe Satalino ◽  
G. Ria
Keyword(s):  
Image Classification ◽  
Remotely Sensed ◽  
Classification Task ◽  
Experimental Comparison ◽  
Remotely Sensed Image

scholarly journals Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles

2014 ◽  
Vol 52 (8) ◽  
pp. 5122-5136 ◽  
Author(s):  
Benqin Song ◽  
Jun Li ◽  
Mauro Dalla Mura ◽  
Peijun Li ◽  
Antonio Plaza ◽  
...  
Keyword(s):  
Image Classification ◽  
Remotely Sensed ◽  
Sparse Representations ◽  
Remotely Sensed Image ◽  
Morphological Attribute

scholarly journals A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

2018 ◽  
Vol 140 ◽  
pp. 133-144 ◽  
Author(s):  
Ce Zhang ◽  
Xin Pan ◽  
Huapeng Li ◽  
Andy Gardiner ◽  
Isabel Sargent ◽  
...  
Keyword(s):  
Image Classification ◽  
Remotely Sensed ◽  
Remotely Sensed Image ◽  
Fine Resolution

scholarly journals A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1451-1454 ◽  
Author(s):  
C. Zhang ◽  
X. Pan ◽  
S. Q. Zhang ◽  
H. P. Li ◽  
P. M. Atkinson
Keyword(s):  
High Resolution ◽  
Decision Tree ◽  
Image Classification ◽  
Rough Set ◽  
Remotely Sensed ◽  
Classification Performance ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Remotely Sensed Image ◽  
Very High

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. <br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

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