An Overview of Technological Revolution in Satellite Image Analysis <s> Object based Image Analysis <s> Abstract Over the last decade the analysis of Earth observation data has evolved from what were predominantly per-pixel multispectral-based approaches, to the development and application of multiscale object-based methods. To empower users with these emerging object-based approaches, methods need to be intuitive, easy to use, require little user intervention, and provide results closely matching those generated by human interpreters. In an attempt to facilitate this, we present multiscale object-specific segmentation (MOSS) as an integrative object-based approach for automatically delineating image-objects (i.e., segments) at multiple scales from a high-spatial resolution remotely sensed forest scene. We further illustrate that these segments cognitively correspond to individual tree crowns, ranging up to forest stands, and describe how such a tool may be used in computer-assisted forest inventory mapping. MOSS is composed of three primary components: object-specific analysis (OSA), object-specific upscaling (OSU), and a new segmentation algorithm referred to as size constrained region merging (SCRM). The rationale for integrating these methods is that the first two provide the third with object-size parameters that otherwise would need to be specified by a user. Analysis is performed on an IKONOS-2 panchromatic image that represents a highly fragmented forested landscape in the Sooke Watershed on southern Vancouver Island, BC, Canada. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Object based Image Analysis <s> This paper presents an approach for detecting the damaged buildings due to earthquake using the watershed segmentation of the post-event aerial images. The approach utilizes the relationship between the buildings and their cast shadows. It is based on an idea that if a building is damaged, it will not produce shadows. The cast shadows of the buildings are detected through an immersion-based watershed segmentation. The boundaries of the buildings are available and stored in a GIS as vector polygons. The vector-building boundaries are used to match the shadow casting edges of the buildings with their corresponding shadows and to perform assessments on a building-specific manner. For each building, a final decision on the damage condition is taken, based on the assessments carried out for that building only. The approach was implemented in Golcuk, one of the urban areas most strongly hit by the 1999 Izmit, Turkey earthquake. To implement the approach, a system called the Building-Based Earthquake Damage Assessment System was developed in MATLAB. Of the 284 buildings processed and analysed, 229 were correctly labelled as damaged and undamaged, providing an overall accuracy of 80.63%. <s> BIB002 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Object based Image Analysis <s> Abstract Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes. <s> BIB003 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Object based Image Analysis <s> Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC. <s> BIB004 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Object based Image Analysis <s> Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process. <s> BIB005
The rate of interest in OBIA based methods increased after 2006. The OBIA methods depend on both the spatial and spectral information of images through considering texture, pixel proximity and geometric attributes of image objects. Jose M. Pena, (2014), mentioned that conventional ML methods are generally based on spectral information but the recent classifiers are mostly object level which considers both the spectral and spatial features  . T. Blaschke, (2010) stated that use of OBIA is more prevalent than perpixel methods and Image segmentation is the major building block of OBIA BIB003 . Hay et al., (2005) performed a higher level of classification using segmented objects to classify the forestland cover into individual tree crowns BIB001 . Muhammad Shahbaz et al., (2012), performed object based image classification using decision trees and achieved higher degree of robustness and accuracy for object recognition  . Rutherford V. Platt, (2008) stated that their proposed approach on OBIA and the performance is superior than per-pixel method for mixed urban and agro land cover classification  . Petra Helmholz et al., (2014) performed semi-supervised classification in high-resolution satellite images through the combination of both pixel and object based classification. Initially, pixel based classification is done using Markov Random Field to extract agricultural regions. Later these extracted regions are classified using SVM BIB005 . Turker et al., (2008) proposed an approach to detect damaged building in an earthquake in Turkey by 1999 using OBIA. The post event aerial images are applied with watershed segmentation to be classified as damaged or undamaged BIB002 .  used the pre-war images acquired from IKONOS for both Jenin and Brest of palestenian territory. The mathematical morphological operators are applied for automated identification of damaged areas. The results are more superior in OBIA than per-pixel approach  . L. Monika Moskal et al., (2011) analyzed the aerial datasets using OBIA for tree cover assessments. The results achieved are compared with that of pixel based analysis and OBIA is found to be superior BIB004 . Normally, in moderate resolution images every individual pixel may cover large spatial region and this increased data resolution may lead to challenges while using traditional approaches. Hence, OBIA is found to be more prominent that includes the use of both spectral & spatial data, which is limited in per-pixel analysis of spectral information.