Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Ontology-based semantic classification of satellite images: Case of major disasters

The International Charter 'Space and Major Disasters' is regularly activated during a catastrophic event and offers rescue teams comprehensive damage maps. Most of these maps are built by means of satellite image manual processing, which is often complex and demanding in terms of time and energy. Automatic processing supplies prompt treatment. Nevertheless, it usually presents a semantic gap handicap. The exploitation of ontologies to bridge the semantic gap has been widely recommended due to their quality of knowledge representation, expression, and discovery. In this work, we present an ontology-based semantic hierarchical classification method to undertake this problem. Ontology components are translated to image-based parameters and exploited to assist the classification process at two levels, and using 12 classes. The region of interest is selected from the first level and exhaustively analyzed and classified at the second level. The 2010 Haiti earthquake was selected as study area for this work. Experiments were performed using very high resolution multi-temporal QuickBird imagery and eCognition software

Geographic ontology for major disasters: methodology and implementation

During a catastrophic event, the International Charter1 "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy

Towards a Semantic Understanding of very High- Resolution Satellite Images: the Case of Major Disasters

The lack of knowledge on damage extent and damage level of affected areas following a major disaster impedes the delivery of the necessary support in guiding rescue teams on the ground, delimiting the extent and level of damaged buildings, spotting the best location for refugee camps, and selecting effective access roads. The increased accessibility of VHR satellite imagery offers new perspectives for the remote sensing and disaster management communities. RS technologies allow fast, effective, and accurate observations of the affected areas. However, these observations need to be rapidly inspected and interpreted to deliver the necessary support. The International Charter "Space and Major Disasters" is activated for this purpose to provide the rescue teams with ready damage maps prepared by means of manual processing and interpretation of satellite images by photo interpreters. A complex, lengthy, and demanding task, which is also subject to errors and subjectivity. Automatic/semiautomatic tools are good alternatives. Automatic processing offers the required prompt treatment intended in such critical situations, nonetheless, it generally presents a semantic gap drawback. The objective of this work is the incorporation of semantics into RS and GIS applications to express and represent expert knowledge in an automatic way. A global ontology that allows geographic and disaster-related knowledge representation, expressivity, and discovery is developed with expert knowledge in remote sensing, disasters, and geographic domains. The approach is based on (i) the conceptualisation of domain knowledge and information surrounding the context, (ii) the development of a global ontology including eight sub-ontologies representing the characteristics of the different related interdomains, (iii) the development of an ontology-based VHR satellite image classification technique based on GEOBIA, and (iv) the application of the ontology and the previous classification results for change detection and damage assessment. A case study on Haiti 2010 earthquake is demonstrated, and the strengths and limitations of the approach are discussed. The results validate the impact of the ontologies in the geographic, remote sensing, and disaster management fields