Journal Article10.1080/014311697218782
Category classification method using a self-organizing neural network
Yosuke Ito,Sigeru Omatu +1 more
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TL;DR: A new category classification method is applied to remote sensing data that employs both a selforganizing neural network and a k -nearest neighbour method and obtains superior classification results compared to other methods.
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Abstract: We apply a new category classification method to remote sensing data. This is a supervised and non-parametric method and employs both a selforganizing neural network and a k -nearest neighbour method. One of the features of the category is represented by the neuron weights after training the neural network based on a competitive learning role. From experimental results, we can see that the proposed method obtains superior classification results compared to other methods.
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The application of artificial neural networks to the analysis of remotely sensed data
Jean-François Mas,Juan J. Flores +1 more
TL;DR: An overview of the main concepts underlying ANNs, including the main architectures and learning algorithms, are presented, and the main tasks that involve ANNs in remote sensing are described.
604
Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion
TL;DR: Simulation results validate that the proposed AFERS is more efficient as compared to the existing approaches and the recognition results obtained from fused features are found to be distinctly superior to both recognition using individual features as well as recognition with a direct concatenation of the individual feature vectors.
189
Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong
Xiaoling Chen,Xiaoling Chen,Yok Shueng Li,Zhigang Liu,Kedong Yin,Zhilin Li,Onyx Wh. Wai,Bruce King +7 more
- 01 Oct 2004
TL;DR: In this article, the spatial patterns of water quality were studied by integrating a Landsat TM image, 58 in situ water quality datasets and 30 samples from two concentration maps derived from SeaWiFS and NOAA/AVHRR images in the Pearl River estuary and the adjacent coastal waters of Hong Kong.
83
Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs
TL;DR: The analysis of two different classification strategies, based on support vector machines and Kohonen's self-organizing maps, reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results.
The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self‐Organizing Map for Supervised Classification
Zhe Li,J. Ronald Eastman +1 more
TL;DR: The problem and nature of unlabelled neurons in the use of SOM for supervised classification is addressed and an auxiliary algorithm proposed here for assigning classes to unlabelling neurons performs with the same success as that experienced with Maximum Likelihood.
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