Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

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A survey of image classification methods and techniques for improving classification performance

Image processing with neural networks–a review

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

The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems☆

In this paper, we propose a pattern classification method for remote sensing data using both a neural network and knowledge-based processing. A neural network has the ability to recognize complex patterns, and classifies them to one of the classes. However,the neural network might produce misclassification. A knowledge-based system which uses human geographical knowledge improves the classification results, compared with a conventional statistical method. The disadvantage of using a knowledge-based system is that it needs a large amount of knowledge to classify the data correctly. We propose a pattern classification method that integrates the advantages of both the neural network and knowledge-based system. The proposed system is divided into two subsystems which consist of recognition and error correction. We use the neural network for classification and the knowledge-based system for correcting misclassification created by the neural network. Experimental results are shown to illustrate the per...

Remote sensing image analysis using a neural network and knowledge-based processing

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

Symmetrical images in the x and/or y directions give zero values for odd regular moments, which cause classification problems. It is proposed that regular moments be computed from centres other than the image centroid, where the centre satisfies the translation, scale, rotation, and/or reflection invariance of regular moments.

Regular moments for symmetric images

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