Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.

An essay towards solving a problem in the doctrine of chances. [Facsimil]

The Dempster-Shafer (D-S) theory has been gaining popularity in fields where incomplete knowledge is a factor. The author explores the application of the D-S theory in system reliability and safety. Inappropriate application of the D-S theory to safety-control policies can degrade plant safety. This is proven in two phases: (1) a unified combination rule for fusing information on plant states given by independent knowledge sources such as sensors or human operators is developed; and (2) combination rules cannot be chosen in an arbitrary manner, i.e., the best choice of combination rules depends on whether the safety-control policy is fault-warning or safety-preservation. >

Interdependence between safety-control policy and multiple-sensor

Change detection is a vibrant area of research in remote sensing. Thanks to increases in the spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can now be effectively detected. However, change detection from very-high-spatial-resolution (VHR) (≤5 m) remote sensing images is challenging due to limited spectral information, spectral variability, geometric distortion, and information loss. To address these challenges, many change detection algorithms have been developed. However, a comprehensive review of change detection in VHR images is lacking in the existing literature. This review aims to fill the gap and mainly includes three aspects: methods, applications, and future directions.

Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions

Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

Dans ce travail on s’interesse a mettre en œuvre un systeme de supervision interne pour les differents signaux: ASI, satellitaire, TNT et SDI ,de telle maniere a rassembler les technologies de l'informatique, de l'electronique et des telecommunications, afin de permettre un controle a distance d'installations techniques geographiquement reparties ou isolees utilisant une panoplie d'outils : Alarme ; etre alerte automatiquement en cas de panne ou de defaut de fonctionnement d'un signal. Conduite ; controler en permanence le fonctionnement des differents signaux. Maintenance ; assurer a distance les tâches de maintenance de certaines installations en cas de defaillance et par consequent intervenir a temps. Enregistrer les informations dans une bade de donnees afin d'analyser, d'optimiser et de gerer a distance le fonctionnement des installations controlees.

Traitement du signal et des images

Image segmentation is an essential step toward higher level image processing in remote sensing. However, the traditional image segmentation approaches based on pixels spectral characteristics and single-scale image information extraction methods have obvious flaws in this respect. Currently, multi-scale image segmentation is seen as a promising alternative of traditional segmentation method and is one of the most useful approaches in object oriented classification of remotely sensed images. In this paper, we present a multi-scale segmentation method based on Minimum Heterogeneity Rule (MHR) for merging objects. Segmentation results show that this method can easily adapt its scale parameter to different scale image analysis tasks and any chosen scale object-extraction of interest.

Multi-scale segmentation for remote sensing imagery based on minimum heterogeneity rule

The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification concept is generally used through eCognition software [1]. In this paper, we propose an object-oriented classification system based on SVM approach. By using a suitable scale during a multi-resolution segmentation step, obtained results are compared to those produced by a pixel-based SVM classifier. The classification process is performed by using a high spatial resolution imagery acquired by the Algerian satellite ALSAT-2A. From the comparison of obtained results, it is concluded that the object-based classifier is more efficient than the pixel-based classifier for the discrimination of seven major land cover classes.

Object-oriented SVM classifier for ALSAT-2A high spatial resolution imagery: A case study of algiers urban area

The aim of this work is to evaluate the performances of three Bayesian networks widely used for supervised image classification. The developed structures are constructed due to Kruskal algorithm which allows the determination of the maximum weight spanning tree by using the mutual information between the attributes. We started by the Bayesian naive classifier (BNC), which assumes that there is no dependency, between the attributes to classify. In order to relax this strong assumption, we tested the tree augmented naive Bayes classifier (TANC) where each feature has at most one variable as parent, and the forest augmented naive Bayes classifier (FANC) where each attribute forms an arbitrary graph rather than just a tree. These classifiers are evaluated using a multispectral image and hyperspectral image in order to analyze the structure classifier complexity according to the number of attributes (04 and 10 spectral bands for the two images respectively). Obtained results are compared with state-of-the art competitor, namely, the SVM classifier. Classified images by TANC and FANC achieved higher accuracies than other classifiers including SVM. It is concluded that the choice of attributes dependencies significantly contributes to the discrimination of subjects on the ground. Thus, Bayesian networks appear as powerful tool for multispectral and hyperspectral image classification.

Supervised classification of remotely sensed images using Bayesian network models and Kruskal algorithm

In this paper, we present fusion and classification process of change indices using multitemporal satellites images in the aim to detect the change of surface states after a flood. This process is performed in the framework of Dempster Shafer Theory (DST), which takes into account the imprecision and the ignorance related to data. We apply this process to a study site located at south west of England, traversed by Severn river, which have undergone in October 2000 an important flood. For the detection of the flood damage, we have used two change indices: difference values and texture evolution. We find that change index fusion overcomes the limits of change mono-index classification.

Change detection approach using evidential fusion of change indices

This paper deals with an unsupervised approach for land change detection and extraction using bitemporal and multispectral remotely sensed images. It is a statistical approach based on multivariate alteration detection (MAD) transformation combined with a new ChiMerge thresholding method. As opposed to most other multivariate change detection schemes the MAD technique is invariant to affine transformations of the originally measured variables. Therefore, it is insensitive to linear differences in atmospheric conditions or sensor calibrations of multitemporal acquisitions. ChiMerge thresholding method is applied to automatically extract change detected by means of MAD analysis. A case study with SPOT-HRV multispectral images before and after a flood event occurred in November 2000 over Gloucester (UK) region shows the power of the proposed MAD/ChiMerge change detection scheme compared to the classical principal component analysis of simple difference combined to the same thresholding method PCA/ChiMerge. A comparison of extracted change objects is also carried out according to the ground truth of the study zone.

Radja Kheddam

Papers

Multi-scale segmentation for remote sensing imagery based on minimum heterogeneity rule

Object-oriented SVM classifier for ALSAT-2A high spatial resolution imagery: A case study of algiers urban area

Supervised classification of remotely sensed images using Bayesian network models and Kruskal algorithm

Change detection approach using evidential fusion of change indices

Multivariate alteration detection and ChiMerge thresholding method for change detection in bitemporal and multispectral images