Very High Resolution Satellite Images Research Articles

Indicators of landscape diversity derived from remote sensing based land cover maps – spatial and thematic aspects

Landscape is heterogeneous part of the Earth surface, forming mosaic of various habitats organized at different scales and levels (Johnson et al. 1992). The landscape pattern has important impact on ecological processes; hence its analysis through quantitative measures is essential for environmental studies. There are many indicators characterizing spatial structure of landscape at different level of detail; they enable analysis of landscape fragmentation at patch level, through studies at habitat level up to complex analyses at landscape level. Seven indicators, which are related to various levels of detail, were selected at the presented work. The following indicators have been studied: Patch Density, Edge Density, Patch Richness, Simpson Diversity Index, Natural Patch Richness, Percentage of Natural Landscape, Mean Natural Patch Area (McGarigal & Marks 1995). First two indicators were used for analysis of landscape fragmentation at patch level, next two at land cover level, while the last three were applied for studies of natural and semi-natural classes at both levels. The studies were performed at six test areas located in different regions of Europe (France, Germany, Poland, Latvia, Spain and Italy), using two different land cover maps. First map was based on Very High Resolution (VHR) Kompsat satellite images (4 m spatial resolution); it included 8 land cover categories with 0.25 ha Minimum Mapping Unit (MMU). CORINE Land Cover (CLC) map 2006 (25 ha MMU) was the second map used for analyses. Number of land cover classes in case of CLC map varied from 9 for Poland till 14 for France. All above mentioned indicators were calculated for grids with 100, 200, 500 and 1000 meter cell size, corresponding to 1, 4, 25 and 100 ha, respectively. The obtained results reveal high usefulness of land cover maps based on VHR satellite images for analysis of landscape fragmentation, even for grids with 100 m cell size. It was found that at patch level these materials are superior to CLC classifications, irrespective of cell area. In case of land cover level VHR data are better while using 100 and 200 m grid cells, whereas for larger cell sizes – 500 and 1000 m – results are not so evident, depending on degree of landscape fragmentation and spatial structure characteristic for individual land cover classes.

Combined application of pansharpening and enhancement methods to improve archaeological cropmark visibility and identification in QuickBird imagery: two case studies from Apulia, Southern Italy

Launch of Very High Resolution (VHR) satellite sensors meant a significant evolution for Remote Sensing archaeological applications since data with high spatial resolution may be used to identify signals related to buried archaeological remains (e.g. cropmarks, soilmarks, dampmarks) in very extended territories. The aim of this work is to develop a suitable image processing workflow to improve the cropmark visibility in the VHR satellite images. The workflow implemented consists of two steps: first the panchromatic and multispectral images have been pansharpened in order to merge the two images, then some Enhancement methods have been applied on the pansharpened image. As a result of the several tested pansharpening techniques, the Principal Component Resolution Merge yields images of very good quality useful not only for visualization but also as input for further processing. Among the different enhancement methods taken into consideration, the Spatial Enhancement, carried out by applying the Wallis Adaptive filter further improve the visibility and recognition of the archaeological signals.All methods were evaluated on QuickBird imagery acquired in the Tavoliere plain (Apulia, Southern Italy) on May 27th, 2006. In particular, cropmarks generated by two buried ancient settlements were considered. For one of these test sites, results of a high resolution ground magnetic survey, carried out for other aims, were also employed to further assess the validity of information extracted by the proposed workflow.

Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling

The maintenance and restoration of high-quality habitats of wildlife species in alpine ecosystems are key issues in conservation biology. The rock ptarmigan (Lagopus muta helvetica), which prefers open habitats above the treeline, is listed in Annex II of the EU Bird Directive. Large areas identified as potentially important for conservation and restricted financial resources for the implementation of conservation activities necessitate the development of tools supporting habitat monitoring and management. We developed a knowledge-based Habitat Suitability Index (HSI) model for rock ptarmigan and combined it with results of an object-based image analysis of very high resolution (VHR) satellite images (IKONOS) to create a rock ptarmigan habitat suitability map in the eastern Alps of Austria. The mechanistic habitat model contained 9 habitat variables, decisive for summer habitat use of rock ptarmigan (i.e., slope, patchiness, and land cover classes for: rocks, rocks intermixed with vegetation, scree, dwarf shrubs, dwarf pine, alpine/subnival grassland, and forest). We used Definiens Professional 5.0© software for the object-based image analyses, applying multi-resolution segmentation methods. We generated a classification hierarchy comprising the same variables used in the HSI model, each augmented by areas “in shadow”, and took into account clouds, water bodies and human infrastructures. We assessed classification accuracies, applying an Error Matrix based on TTA Mask. We reached an overall classification accuracy of 0.75 and a kappa statistic value of 0.70, the latter indicating good to very good agreement. The classification results indicated that the object-oriented image classification approach using VHR data was appropriately used to create an adequate thematic map for further habitat modelling. We calculated the habitat suitability maps using MapModels. Model output was validated with ptarmigan presence–absence data, using signs (droppings) as indicators of presence. We compared presence–absence data and results of habitat suitability classification employing contingency tables and non-parametric correlations. Frequencies of sample plots with rock ptarmigan signs significantly differed between the habitat suitability classes and significantly correlated with the HSI level. Combining the mechanistic HSI model with an object-base image analysis of VHR satellite images was an effective tool for the spatially explicit assessment of habitat suitability and could be useful in regional monitoring, planning and management activities for ptarmigan.

Land cover mapping using aerial and VHR satellite images for distributed hydrological modelling of periurban catchments: Application to the Yzeron catchment (Lyon, France)

The rapid progression of urbanization in periurban areas affects the hydrological cycle of periurban rivers. To quantify these changes, distributed hydrological modelling tools able to simulate the hydrology of peri-urban catchments are being developed. Land cover information is one of the data sources used to define the model mesh and parameters. The land cover in periurban catchments is characterized by a very large het-erogeneity, where the vegetated and the artificial surfaces are finely overlapping. The study is conducted in the Yzeron catchment (150 km 2), close to the city of Lyon, France. We explore the potential of very high-resolution (VHR) optical images (0.50–2.50 m) for retrieving information useful for those distributed hydrological models at two scales. For detailed object-oriented models, applicable to catchments of a few km 2 , where hydrological units are based on the cadastral units, manual digitizing based on the 0.5 m resolution image, was found to be the most accurate to provide the required information. For larger catchments of about 100 km 2 , three semi-automated mapping procedures (pixel based and object-oriented classifications), applied to aerial images (BD-Ortho O IGN), and two satellite images (Quickbird and Spot 5) were compared. We showed that each image/processing provided some interesting and accurate information about some of the land cover classes. We proposed to combine them into a synthesis map, taking profit of the strength of each image/processing in identifying the land cover classes and their physical properties. This synthesis map was shown to be more accurate than each map separately. We illustrate the interest of the derived maps in terms of distributed hydrological modelling. The maps were used to propose a classification of the Yzeron sub-catchments in terms of dominant vegetation cover and imperviousness. We showed that according to the image processing and images characteristics, the calculated imperviousness rates were different. This can lead to significant differences in the hydrological response.

Hierarchical data representation structures for interactive image information mining

In this article an interactive image information mining protocol is presented aiming at a computationally efficient pattern interpretation. The method operates on very high resolution (VHR) remote-sensing optical imagery and follows a modular approach. Images are projected onto a hierarchical image representation structure, the Max-Tree, which interfaces multi-dimensional features of the image components. Positive and negative samples are selected interactively from the image space and are translated into features describing best the targeted and non-desired patterns. Sourcing the feature entries into a hierarchical clustering algorithm, the kd-Tree, yields a structured representation that ensures fast classification. A classification is computed directly from the kd-Tree and is applied on the Max-Tree for accepting or rejecting image components. The complete process cycle is demonstrated on gigapixel-sized VHR satellite images and requires 3 min for building the Max-Tree, 30 min for hierarchical clustering and less than 10 s for each example based query.

OPTIMIZING OBJECT-BASED CLASSIFICATION IN URBAN ENVIRONMENTS USING VERY HIGH RESOLUTION GEOEYE-1 IMAGERY

Abstract. The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In fact, one of the most common applications of remote sensing images is the extraction of land cover information for digital image base maps by means of classification techniques. When VHR satellite images are used, an object-based classification strategy can potentially improve classification accuracy compared to pixel based classification. The aim of this work is to carry out an accuracy assessment test on the classification accuracy in urban environments using pansharpened and panchromatic GeoEye-1 orthoimages. In this work, the influence on object-based supervised classification accuracy is evaluated with regard to the sets of image object (IO) features used for classification of the land cover classes selected. For the classification phase the nearest neighbour classifier and the eCognition v. 8 software were used, using seven sets of IO features, including texture, geometry and the principal layer values features. The IOs were attained by eCognition using a multiresolution segmentation approach that is a bottom-up regionmerging technique starting with one-pixel. Four different sets or repetitions of training samples, always representing a 10% for each classes were extracted from IOs while the remaining objects were used for accuracy validation. A statistical test was carried out in order to strengthen the conclusions. An overall accuracy of 79.4% was attained with the panchromatic, red, blue, green and near infrared (NIR) bands from the panchromatic and pansharpened orthoimages, the brightness computed for the red, blue, green and infrared bands, the Maximum Difference, a mean of soil-adjusted vegetation index (SAVI), and, finally the normalized Digital Surface Model or Object Model (nDSM), computed from LiDAR data. For buildings classification, nDSM was the most important feature attaining producer and user accuracies of around 95%. On the other hand, for the class "vegetation", SAVI was the most significant feature, obtaining accuracies close to 90%.

Fusion of Textural and Spectral Information for Tree Crop and Other Agricultural Cover Mapping With Very-High Resolution Satellite Images

A new procedure is proposed for agricultural land-use mapping that addresses a known weakness of classical per-pixel methods in situations involving mixed tree crops. The proposed scheme uses a pair of very-high resolution satellite-borne panchromatic and multispectral images and integrates classification results of two parallel and independent analyses, respectively based on spectral and textural information. The multispectral image is divided into spectrally homogeneous but non-contiguous segments using unsupervised classification. In parallel, the panchromatic image is split into a grid of square blocks on which is performed a texture-driven supervised classification. Finally, the spectral and the textural classifications are fused to generate the land-use map. This method contrasts with object-based methods that sequentially perform image segmentation and classification. Results are evaluated both quantitatively and qualitatively, based on field survey ground-truth data. The quantitative assessment is presented in terms of overall accuracy (from 80% to 100% depending on the area) and Kappa coefficients. Visual comparison of the resulting map with the ground-truth is performed, with the analysis of the binary error maps. Merging spectral and textural classifications results in finer border delimitation and improves the overall classification accuracy of agricultural land-use by 27% as compared to textural classification alone.

A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.