Automated parameterisation for multi-scale image segmentation on multiple layers

AbstractWe introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis

Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

Mapping or "delimiting" landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results. © 2014 The Authors

Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

Mapping or "delimiting" landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results

Supervised methods of image segmentation accuracy assessment in land cover mapping

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed

Semi-automated Extraction of Landslides in Taiwan Based on SPOT Imagery and DEMs

[[abstract]]The vast availability and improved quality of optical satellite data and digital elevation models (DEMs), as well as the need for complete and up-to-date landslide inventories at various spatial scales have fostered the development of semi-automated landslide recognition systems. Among the tested approaches for designing such systems, object-based image analysis (OBIA) stepped out to be a highly promising methodology. OBIA offers a flexible, spatially enabled framework for effective landslide mapping. Most object-based landslide mapping systems, however, have been tailored to specific, mainly small-scale study areas or even to single landslides only. Even though reported mapping accuracies tend to be higher than for pixelbased approaches, accuracy values are still relatively low and depend on the particular study. There is still room to improve the applicability and objectivity of object-based landslide mapping systems. The presented study aims at developing a knowledge-based landslide mapping system implemented in an OBIA environment, i.e. Trimble eCognition. In comparison to previous knowledge-based approaches, the classification of segmentation-derived multi-scale image objects relies on digital landslide signatures. These signatures hold the common operational knowledge on digital landslide mapping, as reported by 25 Taiwanese landslide experts during personal semi-structured interviews. Specifically, the signatures include information on commonly used data layers, spectral and spatial features, and feature thresholds. The signatures guide the selection and implementation of mapping rules that were finally encoded in Cognition Network Language (CNL). Multi-scale image segmentation is optimized by using the improved Estimation of Scale Parameter (ESP) tool. The approach described above is developed and tested for mapping landslides in a sub-region of the Baichi catchment in Northern Taiwan based on SPOT imagery and a high-resolution DEM. An object-based accuracy assessment is conducted by quantitatively comparing extracted landslide objects with landslide polygons that were visually interpreted by local experts. The applicability and transferability of the mapping system are evaluated by comparing initial accuracies with those achieved for the following two tests: first, usage of a SPOT image from the same year, but for a different area within the Baichi catchment; second, usage of SPOT images from multiple years for the same region. The integration of the common knowledge via digital landslide signatures is new in object-based landslide studies. In combination with strategies to optimize image segmentation this may lead to a more objective, transferable and stable knowledge-based system for the mapping of landslides from optical satellite data and DEMs

Expert Knowledge for Object-Based Landslide Mapping in Taiwan

[[abstract]]With object‐based image analysis (OBIA) landslides can be mapped more accurately than with pixel‐based methods. While many authors have recognized the value of segmentation optimization for increasing the objectivity and transferability of landslide mapping, the optimization of the classification step is lagging behind. This study introduces a landslide mapping system that is based on expert knowledge models and implemented in OBIA. These models hold the operational knowledge about landslides and digital landslide mapping such as data, classification features, and feature thresholds. The knowledge was gathered during personal semi‐structured interviews of 20 Taiwanese landslide experts. The system was tested for mapping landslides in a sub‐region of the Baichi catchment in Northern Taiwan. The potential landslide areas were accurately extracted. The refinement of the potential landslide area into landslide types was mainly based on slope values. Additional expert‐based rules will be implemented to increase the accuracy and objectivity of the final landslide classification

Object representations at multiple scales from digital elevation models

In the last decade landform classification and mapping has developed as one of the most active areas of geomorphometry. However, translation from continuous models of elevation and its derivatives (slope, aspect, and curvatures) to landform divisions (landforms and landform elements) is filtered by two important concepts: scale and object ontology. Although acknowledged as being important, these two issues have received surprisingly little attention

Terrain extraction in built-up areas from satellite stereo-imagery-derived surface models : a stratified object-based approach

Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observedespecially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM andin accordance with the RUIdiffers between the different land cover classes.G-SEXTANT FP7 312912FWF Doctoral College GIScience (DK W 1237-N23)(VLID)170229

A Proposal for Mapping Historic Irrigation Channels to Reveal Insights into Agro-Climatic Systems: A Case Study in Upper Austria. GI_Forum 2013 – Creating the GISociety|

Recently, the remains of two historic irrigation channels were re-discovered in the Upper Austrian municipality of Regau. Since the current average precipitation in the region is sufficient to sustain a productive agricultural land use, the irrigation channels raise several questions related to climate variability. To verify different hypotheses such as the construction as a response to a changing climate or the assumed purpose of grassland irrigation, potential coherences are discussed. In addition, remote sensing techniques for the detection of hidden structures overprinted by physical and human activities are outlined. The analysis of available references enables a first assessment of the channels purpose. Several indicators such as the shape and incline of the remaining topographic imprints support the hypothesis of their irrigation function. Furthermore, the qualitative analysis of today’s agro-climatic conditions reveals the general vulnerability of the system to climate changes. Moreover, a dendrochronological analysis shows a phase of remarkable climate variability in conjunction with significant drying periods throughout the 3rd and 4th century. This may have posed a reason for the construction of the irrigation system. Further research is necessary to verify the possible alternative hypothesis, which suggests grassland fertilization as the main irrigation purpose. Therefore, a long term analysis of water nutrient contents is required. Moreover, the application of remote sensing techniques may help to identify the extent of the formerly irrigated area

A Proposal for Mapping Historic Irrigation Channels to Reveal Insights into Agro-Climatic Systems: A Case Study in Upper Austria. GI_Forum 2013 – Creating the GISociety|

Recently, the remains of two historic irrigation channels were re-discovered in the Upper Austrian municipality of Regau. Since the current average precipitation in the region is sufficient to sustain a productive agricultural land use, the irrigation channels raise several questions related to climate variability. To verify different hypotheses such as the construction as a response to a changing climate or the assumed purpose of grassland irrigation, potential coherences are discussed. In addition, remote sensing techniques for the detection of hidden structures overprinted by physical and human activities are outlined. The analysis of available references enables a first assessment of the channels purpose. Several indicators such as the shape and incline of the remaining topographic imprints support the hypothesis of their irrigation function. Furthermore, the qualitative analysis of today’s agro-climatic conditions reveals the general vulnerability of the system to climate changes. Moreover, a dendrochronological analysis shows a phase of remarkable climate variability in conjunction with significant drying periods throughout the 3rd and 4th century. This may have posed a reason for the construction of the irrigation system. Further research is necessary to verify the possible alternative hypothesis, which suggests grassland fertilization as the main irrigation purpose. Therefore, a long term analysis of water nutrient contents is required. Moreover, the application of remote sensing techniques may help to identify the extent of the formerly irrigated area