An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.

Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL077787

CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network

The problem of cloud data classification from satellite imagery using neural networks is considered. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.

/pdf/a-study-of-cloud-classification-with-neural-networks-using-1dwodnze26.pdf

A study of cloud classification with neural networks using spectral and textural features

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection and classification. An unsupervised Kohonen neutral network was used to classify, the cloud contents of a 8/spl times/8 blocks in an image into ten different cloud classes. Inputs to the network consisted of textural features of each block obtained using an efficient feature extraction scheme namely, the wavelet transform (WT). This scheme not only reduces the dimensionality of the data but also extracts useful features of the data. To improve the detection rate and reduce the false positive rate, especially for low clouds and thin high clouds, a multi-channel fusion system was constructed to combine the results of different optical bands. All alternative approach for automatic cloud detection/classification based on multi-spectral features was also studied to analyze and compare the effectiveness of multi-spectral-based scheme vs textural-based scheme. The results using high resolution GOES 8 data show the promise of the Kohonen neural network when used in conjunction with WT as feature extractor for cloud detection/classification.

Neural network-based cloud detection/classification using textural and spectral features

Automatic cloud classification of satellite imagery can be of great help to meteorological studies. A neural network-based cloud classification system is developed and introduced. Several image transformation schemes such as wavelet transform (WT) and singular value decomposition (SVD) are used to extract the salient textural feature of the data and is then compared with those of the well-known gray-level co-occurrence matrix (GLCM) approach. Two different neural network paradigms namely the probability neural network (PNN) and the unsupervised Kohonen (1990) self-organized feature map (SOM) are chosen and examined. The performance of the proposed cloud classification system is benchmarked on the Geostationary Operational Environmental Satellite (GOES) 8 data set and promising results have been achieved.

Neural network-based cloud classification on satellite imagery using textural features

Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.

/pdf/comparison-of-two-different-pnn-training-approaches-for-1lm8jdaor0.pdf

Comparison of two different PNN training approaches for satellite cloud data classification

This paper presents a new training algorithm for probabilistic neural networks (PNNs) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood learning on a cloud classification problem using satellite imagery data.

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A study of cloud classification with neural networks using spectral and textural features

Neural network-based cloud detection/classification using textural and spectral features

Neural network-based cloud classification on satellite imagery using textural features

Comparison of two different PNN training approaches for satellite cloud data classification

Comparison of two different PNN training approaches for satellite cloud data classification