Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis.

https://www.tandfonline.com/doi/pdf/10.1080/01431160701352154

The application of artificial neural networks to the analysis of remotely sensed data

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

Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. With the increasing availability of remote sensing data, numerous methods have been developed for damage assessment. This article gives a comprehensive review of these techniques focusing on multi-temporal SAR procedures for rapid damage assessment: interferometric coherence and intensity correlation. The review is divided into six parts: First, methods based on coherence; second, the ones using intensity correlation; and third, techniques using both methodologies combined to increase the accuracy of the damage assessment are reviewed. Next, studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy are reported. Moreover, selected studies on post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence are presented. Then, the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions are reported. Finally, an outlook to the Sentinel-1 SAR mission shows possible solutions of these limitations, enabling a worldwide applicability of the presented damage assessment methods.

/pdf/rapid-damage-assessment-by-means-of-multi-temporal-sar-a-14rd97xaks.pdf

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper makes three distinct contributions. The proposed deep network consisting of autoencoders and the SOM-based classifier is computationally more efficient and performance wise more accurate. The fusion of geometric features with LBP features using autoencoders provides better representation of facial expression. The SOM-based classifier proposed in this paper has been improved by making use of a soft-threshold logic and a better learning algorithm. The performance of the proposed approach is validated on two widely used databases (DBs): 1) MMI and 2) extended Cohn-Kanade (CK+). An average recognition accuracy of 97.55% in MMI DB and 98.95% in CK+ DB are obtained using the proposed algorithm. 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. Simulation results validate that the proposed AFERS is more efficient as compared to the existing approaches.

Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion

Urbanization has profound effects on the environment at local, regional, and global scales. Effective detection of urban change using remote sensing data will be an essential component of global environmental change research, regional planning, and natural resource management. This paper presents results from an ARTMAP neural network to detect urban change with Landsat TM images from two periods. Classification of urban change, and, in particular, conversion of agriculture to urban, was statistically more accurate with ARTMAP than with a more conventional technique, the Bayesian maximum-likelihood classifier (MLC). The effect of different levels of class aggregation on the performance of change detection was also explored with ARTMAP and MLC. Because ARTMAP explicitly allows “many-to-one” mapping, classification using coarse class resolution and fine class resolution training data generated similar results. Together, these results suggest that ARTMAP can reduce labor and computational costs associated with assembling training data while concurrently generating more accurate urban change-detection results.

Comparing ARTMAP Neural Network with the Maximum-Likelihood Classifier for Detecting Urban Change

A classification method for polarimetric SAR data analysis using a competitive neural network is considered. The network is trained by two LVQ algorithms. In addition, a specific feature vector as the input for the network employing the JM distance is determined. As a result of experiments using SIR-C data, average accuracy for classification results was 86.40%, where (i) the competitive neural network with 8-input and 40-output neurons was trained by LVQ1 and LVQ2.1, and (ii) the 8-dimensional feature vector with backscattering coefficients (dB) and pseudo-relative phases between HH and VV from L and C bands was used. It is shown that the proposed method outperforms other methods in average accuracy.

Polarimetric SAR data classification using competitive neural networks

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.

Category classification method using a self-organizing neural network

A competitive neural network (NN) learned by the OLVQ1 algorithm has the potential to produce images for land cover mapping. We propose an extended OLVQ1 that adds learning ratios to the original algorithm. Applying it to SPOT XS data, we show that higher mapping accuracies can be obtained compared to those of conventional methods.

Extended LVQ Neural Network Approach to Land Cover Mapping

This paper presents a damage estimation model suitable for evaluating earthquake damage using coherence images derived from interferometric SAR data. The damage degree is estimated through temporal decorrelation by employing a coherence ratio which is defined by dividing a post-event coherence image by a pre-event image. The temporal coherence ratio is confirmed to be closely correlated with the probability of the damage degree. As a result of applying both C and L bands SAR data for evaluating the damage of the 1995 Hyogoken-Nanbu Earthquake in Japan, the cumulative probability of the damage degree can be approximated by a linear function of the coherence ratio.

Damage estimation model using temporal coherence ratio

A degree of earthquake damage can be estimated using temporal decorrelation by employing a coherence ratio which is defined by dividing a post-event coherence image by a pre-event image. In the case of applying both C and L bands SAR data for evaluating the damage of the 1995 Hyogoken-Nanbu Earthquake in Japan, the probability of the degree of damage could be approximated by a linear function of the coherence ratio. In this paper, we examine whether the post-earthquake damage estimation model is applicable for the 1999 Kocaeli earthquake in Turkey as another case. As a result, significant correlation between the probability of the degree of damage and the grade of damage surveyed by disaster researchers is also clarified by employing the coherence ratio computed from three ERS- 1/2 SAR data set including the event.

A degree estimation model of earthquake damage using temporal coherence ratio

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