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.

Object based image analysis for remote sensing

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Remote Sensing And Image Interpretation

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A survey on object detection in optical remote sensing images

A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery

On a role

Pixel-based classifications have difficulty adequately or conveniently exploiting expert knowledge or contextual information. Object-based image-processing techniques overcome these difficulties by first segmenting the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels. The segments (objects) are assigned classes using fuzzy logic and a hierarchical decision key. To date, the main drawback has been the lack of effective software. This paper evaluates a simple object-based approach to classifying forest cut blocks near Revelstoke, British Columbia, in a Landsat enhanced thematic mapper plus (ETM+) image using recently released eCognition software. Three subsets of the image test the software. Two subsets are chosen to evaluate it in different land-cover situations, and the third subset is used to test the effectiveness of replicating a rule system developed elsewhere in the image. Rules for class assignment include traditional s...

Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction

For classification purposes, the proposed guideline is a choose Mean and, where a class patch is likely to contain edge-like features within it, Con. Cor is an alternative for Mean in these situations, Dis may similarly be used in place of Con. For more detailed texture study, add Ent. This guideline does not constitute a complete texture analysis but may allow confident use of GLCM texture to enhance the efficiency of Landsat-based classification.

Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

Standardized principal components analysis (SPCA) is performed on normalized difference vegetation index (NDVI) time series of a 10/spl deg/ latitude by 10/spl deg/ longitude area of western Canada including grassland, parkland, and forest ecosystems. When used with input from a single growing season (April-October), early components correspond closely to ecoregional mapping based on long-term vegetation composition. Later components isolate areas showing agricultural practice or climatic stress particular to the year. When three years' growing seasons are input together into a multiyear SPCA, similar patterns occur in the first component. However, components occur early in the series that discriminate areas having different seasonality patterns associated with plant phenology. Deciduous-dominated areas are well distinguished from grasslands. Multiyear SPCA includes an early component apparently related to latitudinal variation in day length, which does not appear in single-year component series. An early component unrelated to any known geographical or climatological pattern or event appears, which may relate to sensor degradation. Both single-year and multiyear SPCA isolate NDVI variability in higher numbered components that is limited in space and/or in time. This allows interpretation of transient or localized events using detailed local data, separating them from regional trends that occur in earlier (lower numbered) components of the series. These results demonstrate the possibility of refining ecoregion mapping based on selected early components to incorporate actual interannual variability for selected time periods as well as the long-term stable elements of biogeoclimatic regions. Longer time series could potentially quantify observed unidirectional trends resulting from climate change.

Comparison of single-year and multiyear NDVI time series principal components in cold temperate biomes

Investigations of land-cover change often employ metrics designed to quantify changes in landscape structure through time, using analyses of land cover maps derived from the classification of remote sensing images from two or more time periods. Unfortunately, the validity of these landscape pattern analyses (LPA) can be compromised by the presence of spurious change, i.e., differences between map products caused by classification error rather than real changes on the ground. To reduce this problem, multi-temporal time series of land-cover maps can be constructed by updating (projecting forward in time) and backdating (projecting backward in time) an existing reference map, wherein regions of change are delineated through bi-temporal change analysis and overlaid onto the reference map. However, this procedure itself creates challenges, because sliver patches can occur in cases where the boundaries of the change regions do not exactly match the land-cover patches in the reference map. In this paper, we describe how sliver patches can inadvertently be created through the backdating and updating of land-cover maps, and document their impact on the magnitude and trajectory of four popular landscape metrics: number of patches (NP), edge density (ED), mean patch size (MPS), and mean shape index (MSI). In our findings, sliver patches led to significant distortions in both the value and temporal behaviour of metrics. In backdated maps, these distortions caused metric trajectories to appear more conservative, suggesting lower rates of change for ED and inverse trajectories for NP, MPS and MSI. In updated maps, slivers caused metric trajectories to appear more extreme and exaggerated, suggesting higher rates of change for all four metrics. Our research underscores the need to eliminate sliver patches from any study dealing with multi-temporal LPA.

https://people.ucalgary.ca/~mcdermid/Docs/Articles/Linke_etal_2009_LE.pdf

The influence of patch-delineation mismatches on multi-temporal landscape pattern analysis

Remote sensing plays a key role in landscape monitoring, but our handling of these data in a multi-temporal time series is not yet fully developed. Of particular concern is the presence of spatial and thematic errors in independently created maps that distort measures of landscape pattern and constrain the reliability of change analysis. In addition, there is a need to incorporate continuous attributes of cover gradients for flexible map representations that support a variety of applications. In this paper, we present a framework for generating temporally and categorically dynamic land-cover maps that provide such a reliable and adaptable foundation. The centerpiece is a spatio-temporal disturbance-inventory database, created through semi-automated change detection and conditioned with boundary-matching procedures, which can be used to backdate and update both continuous and categorical reference maps. We demonstrate our approach using multi-annual Landsat imagery from a forested region in west-central Alberta, Canada, between the years 1998 and 2005.

https://people.ucalgary.ca/~mcdermid/Docs/Articles/Linke_etal_2009_PERS.pdf

Mryka Hall-Beyer

Papers

Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction

Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

Comparison of single-year and multiyear NDVI time series principal components in cold temperate biomes

The influence of patch-delineation mismatches on multi-temporal landscape pattern analysis

A Disturbance-Inventory Framework for Flexible and Reliable Landscape Monitoring