Segmentation of remote sensing images using similarity measure based fusion-MRF model

Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data

Change detection studies in Matang Mangrove Forest area, Perak

In this research wok, three different techniques of change detection were used to detect changes in forest areas. One of the techniques used a local similarity measure approach to detect changes. This new approach of change detection technique, which used mutual information to measure the similarity between two multi-temporal images, was developed based on correspondence of the pixel values, rather than the difference in their intensity. Pixels suffering any changes will be maximally dissimilar. The study was conducted using multi-temporal SPOT 5 satellite images, with the resolution of 10 m x10 m on 5th August 2005 and 13th June 2007. The experimental results show that local mutual information provides more reliable results in detecting changes of the multi-temporal images containing different lighting condition compared to the image differencing and NDVI technique, specifically in areas with less plant growth. In addition, it can also overcome the problem on selecting the threshold value. Besides, the findings of this study have also shown that band 3, which is sensitive to vegetation biomass, gave the best result in detecting area of changes compared to the others

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications

Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possiblechange detectors in multi-sensor configurations

La détection de changement au service de la gestion de catastrophe

Depuis quelque temps, lorsque nous pensons à une catastrophe majeure, qu’elle soit d’ordre naturel ou de notre propre faction, nous pensons presque automatiquement à des images satellitaires des zones affectées. Ceci nous vient à l’esprit en partie à cause de la couverture médiatique qui utilise de plus en plus les mêmes sources de données que celles qui sont utilisées pour aider à la planification des efforts de secours. Le traitement d’images satellitaires est un outil précieux dans ce contexte-ci car nous pouvons extraire de nombreux types d’information pertinents aux diverses étapes de la planification des secours. Les concepts reliés à la télédétection ainsi que les outils et les techniques d’analyse qu’ont développé les chercheurs, les analystes et les photo-interprètes pour traiter et analyser des images satellitaires sont utilisés à bon escient afin de réaliser le traitement et l’analyse rapide d’images lors de catastrophes majeures pour aider à réaliser les produits cartographiques requis par la planification des efforts de secours. Ce document porte sur l’un des aspects techniques qui pourraient être particulièrement judicieux dans ce contexte, la détection de changement. Nous comprenons que l’exercice d’analyse que sous-tend l’usage d’images satellitaires dans un contexte de gestion de catastrophe est essentiellement la comparaison de ce qui « était » avant un événement de ce type à ce qui « est » après une catastrophe majeure. Conceptuellement, cette famille de techniques semble tout à propos, mais qu’en est-il réellement? Pour répondre à cette question, nous nous pencherons sur la question en analysant la chaine de traitement sous-jacente ainsi que les contraintes fonctionnelles s’y rapportant et tenterons de remettre le tout en contexte en fonction de la détection de changement et de la difficulté reliée à son utilisation dans un contexte de gestion de catastrophe. Nous nous pencherons aussi sur la question de l’établissement des éléments techniques, fonctionnels et conceptuels requis pour permettre d’accroitre le potentiel d’utilisation de la détection de changement dans un contexte de gestion de catastrophe

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007