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

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

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

Coupled ocean-atmosphere science steadily advances with increasing information obtained from long-records of in situ observations, multiple-year archives of remotely sensed satellite images, and long time series of numerical model outputs. However, the percentage of data actually used tends to be low, in part because of a lack of efficient and effective analysis tools. For instance, it is estimated that less than 5% of all remotely sensed images are ever viewed by human eyes or actually used (Petrou, 2004). Also, accurately extracting key features and characteristic patterns of variability from a large data set is vital to correctly understanding the interested ocean and atmospheric processes (e.g., Liu & Weisberg, 2005). With the increasing quantity and type of data available in meteorological and oceanographic research there is a need for effective feature extraction methods. The Self-Organizing Map (SOM), also known as Kohonen Map or Self-Organizing Feature Map, is an unsupervised neural network based on competitive learning (Kohonen, 1988, 2001; Vesanto & Alhoniemi, 2000). It projects high-dimensional input data onto a low dimensional (usually two-dimensional) space. Because it preserves the neighborhood relations of the input data, the SOM is a topology-preserving technique. The machine learning is accomplished by first choosing an output neuron that most closely matches the presented input pattern, then determining a neighborhood of excited neurons around the winner, and finally, updating all of the excited neurons. This process iterates and fine tunes, and it is called self-organizing. The outcome weight vectors of the SOM nodes are reshaped back to have characteristic data patterns. This learning procedure leads to a topologically ordered mapping of the input data. Similar patterns are mapped onto neighboring regions on the map, while dissimilar patterns are located further apart. An illustration of the work flow of an SOM application is given in Fig. 1. The SOM is widely used as a data mining and visualization method for complex data sets. Thousands of SOM applications were found among various disciplines according to an early survey (Kaski et al., 1998). The rapidly increasing trend of SOM applications was reported in Oja et al. (2002). Nowadays, the SOM is often used as a statistical tool for multivariate analysis, because it is both a projection method that maps high dimensional data to lowdimensional space, and a clustering and classification method that order similar data patterns onto neighboring SOM units. SOM applications are becoming increasingly useful in geosciences (e.g., Liu and Weisberg, 2005), because it has been demonstrated to be an

/pdf/a-review-of-self-organizing-map-applications-in-meteorology-2eaomxgcv4.pdf

A Review of Self-Organizing Map Applications in Meteorology and Oceanography

Sensory integration or sensor fusion - the integration of information from different modalities, cues, or sensors - is among the most fundamental problems of perception in biological and artificial systems. We propose a new architecture for adaptively integrating different cues in a self-organized manner. In Democratic Integration different cues agree on a result, and each cue adapts toward the result agreed on. In particular, discordant cues are quickly suppressed and recalibrated, while cues having been consistent with the result in the recent past are given a higher weight in the future. The architecture is tested in a face tracking scenario. Experiments show its robustness with respect to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, although all cues may be disrupted at one time or another.

Democratic Integration: Self-Organized Integration of Adaptive Cues

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

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

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

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