CLASSIFICATION OF MULTISENSOR REMOTE-SENSING IMAGES BY STRUCTURED NEURAL NETWORKS

This paper proposes the application of structured neural networks to classification of multisensor remote-sensing images, The purpose of our approach is to allow the interpretation of the ''network behavior,'' as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, our approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process, First of all, the architecture of structured multilayer feedforward networks is tailored to a multisensor classification problem. Then, such networks are trained to solve the problem by the error backpropagation algorithm, Finally, they are transformed into equivalent networks to obtain a simplified representation. The resulting equivalent networks may be interpreted as a hierarchical arrangement of ''committees'' that accomplish the classification task by checking on a set of explicit constraints on input data, Experimental results on a multisensor (optical and SAR) data set are described in terms of both classification accuracy and network interpretation. Comparisons with fully connected neural networks and with the kappa-nearest neighbor classifier are also made

Structured neural networks for signal classification

In this paper, artificial neural networks are considered as an emergent alternative to the classical 'model-based approach' to the design of signal-processing algorithms. After briefly examining the pros and cons of the neural-network approach, we propose the application of structured neural networks (SNNs) for the classification of signals characterized by different 'information sources', such as multisensor signals or signals described by features computed in different domains. The main purpose of such neural networks is to overcome the drawbacks of classical neural classifiers due to the lack of general criteria for 'architecture definition' and to the difficulty with interpreting the 'network behaviour'. Our structured neural networks are based on multilayer perceptrons with hierarchical sparse architectures that take into account explicitly the 'multisource' characteristics of input signals and make it possible to understand and validate the operation of the implemented classification algorithm. In particular, the interpretation of the SNN operation can be used to identify which information sources and which related components are negligible in the classification process. SNNs are compared with both commonly used fully connected multilayer perceptrons and the k-nearest neighbour statistical classifier. Experiments on two multisource data sets related to magnetic-resonance and remote-sensing images are reported and discussed. (C) 1998 Elsevier Science B.V. All rights reserved

A hybrid system for two-dimensional image recognition

In this paper, after briefly considering the reasons that prevented the development of hybrid systems for signal processing (SP), we point out the requirements for their future exploitation. The need for a better knowledge of different approaches in the scientific community and the definition of methodologies for designing hybrid systems' are highlighted as two key points. Then we suggest that the well-blown ''task-structure analysis'' design technique should be modified to make it suitable for hybrid systems. The proposed modification is based on the main need to choose the roles of different approaches and the mechanisms to integrate them. As an example, we describe the design of a hybrid system for two-dimensional (2 D) image recognition; the system is based on the integration of a numerical, a symbolic, and a connectionist approach. We detail the integration of the symbolic and connectionist approaches to the generation of the models of the objects to be recognized. We describe the main problems involved and the solutions adopted. In particular, we exploit the synergistic aspects of the two approaches in oi dei to overcome the bottleneck of knowledge acquisition. Finally, we report experimental results on two applications to show some advantages of the proposed hybrid system

INTELLIGENT CONTROL OF SIGNAL PROCESSING ALGORITHMS IN COMMUNICATIONS

In future telecommunications networks, an important role will be played by ''intelligent control'' techniques aimed at selecting and tuning signal processing (SP) algorithms, In this paper, we first define the main problems of automatic control of SP algorithms; then, we propose our knowledge-based approach to carry out such a task. In the planning phase, a restricted set of algorithm sequences are selected. This set is used as a raw plan to be refined and corrected in the execution phase, based on quality tests on progressive results. In particular, we focus on the control strategies adopted and describe how expert knowledge is represented and applied to implement such strategies, As an example, the control of low-level image processing is detailed, Results an an image-compression application are also reported

An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images RID A-2076-2012

An experimental analysis of the use of different neural models for the supervised classification of multisensor remote-sensing data is presented. Three types of neural classifiers are considered: the Multilayer Perceptron, a kind of Structured Neural Network, proposed by the authors, that allows the interpretation of the network operation, and a Probabilistic Neural Network. Furthermore, the k-nearest neighbour statistical classifier is also considered in order to evaluate the validity of the aforementioned neural networks, as compared with that of classical statistical methods. The results provided by the above classifiers are compared

Radial Basis Function and Multilayer Perceptron Neural Networks for Sea Water Optically active parameter estimation in case II waters: a comparison

This paper deals with the problem of retrieving optically active parameters of the water from multispectral remotely sensed data. We analyse the neural networks approach applied to the estimation of chlorophyll concentration in coastal waters (Case II Waters) and discuss the use of two types of networks: the Radial Basis Function neutral network and Multilayer Perceptron. We present a brief summary concerning their architectures and training methods. For proving the concept we analyse the procedures and the performances on a simulated data set reproducing the data acquired from the MERIS (Medium Resolution Imaging Spectrometer), the multispectral sensor on board the ENVISAT satellite. The multispectral subsurface reflectance data have been generated by means of a three component ocean colour direct model and statistically reproduce the case II waters. The neural networks performances have been analysed in terms of MSE (Mean Square Error), correlation coefficient and relative error. We provide a detailed discussion and comparison of the two types of networks and the obtained results confirm the effectiveness of the neural approach in such an application

Comparison And Combination Of Statistical And Neural Network Algorithms For Remote-Sensing Image Classification

In recent years, the remote-sensing community has became very interested in applying neural networks to image classification and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a "panacea". The superiority of one algorithm over the other strongly depends on the selected data set and on the efforts devoted to the "designing phases" of algorithms. In this paper, we propose the use of "ensembles" of neural and statistical classification algorithms as an alternative approach based on the exploitation of the complementary characteristics of different classifiers. Classification results provided by image classifiers contained in these ensembles are "merged" according to statistical combination methods. Experimental results on a multisensor remotesensing data set point out that the use of classifiers ensembles can constitute a valid alternative to the..

A clustering approach to heterogeneous change detection

Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change