J. A. Recio

Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification

In this paper, a comprehensive set of texture features extracted from the experimental semivariogram of specific image objects is proposed and described, and their usefulness for land use classification of high resolution images is evaluated. Fourteen features are defined and categorized into three different groups, according to the location of their respective parameters in the semivariogram curve: (i) features that use parameters close to the origin of the semivariogram, (ii) the parameters employed extend to the first maximum, and (iii) the parameters employed are extracted from the first to the second maximum. A selection of the most relevant features has been performed, combining the analysis and interpretation of redundancies, and using statistical discriminant analysis methods. The suitability of the proposed features for object-based image classification has been evaluated using digital aerial images from an agricultural area on the Mediterranean coast of Spain. The performance of the selected semivariogram features has been compared with two different sets of texture features: those derived from the grey level co-occurrence matrix, and the values of raw semivariance directly extracted from the semivariogram at different positions. As a result of the tests, the classification accuracies obtained using the proposed semivariogram features are, in general, higher and more balanced than those obtained using the other two sets of standard texture features.

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images

The benchmark problem proposed in this paper is to identify regions in aerial or satellite images with geometric patterns and describe the geometric properties of the constituent elements of the pattern and their spatial distribution. This is a relevant topic in image analysis processes where spatial regular patterns are studied. This paper first presents two approaches based on multi-directional semivariograms for reducing the processing time required to compute omnidirectional semivariograms. A set of parameters for describing the structure of a semivariogram, introduced by Balaguer et al. (2010), is extracted from an experimental semivariogram and analysed to quantify the heterogeneity of the distribution of elements (trees) with periodic patterns in images of orchards. An assessment is made using four image datasets. The first dataset is composed of synthetic images that simulate regularly spaced tree crops and real images, and is used to evaluate the influence that the orientation of elements (regularly spaced trees) in the objects (crop plots) has in the descriptive parameter values. This dataset is also used to compare different semivariogram computational approaches. The other three are also composed of synthetic images and are used to evaluate the semivariogram parameters under different spatial heterogeneity conditions, and are generated by varying patterns and tree characteristics, i.e., existence or absence of faults, regular/irregular distributions, and size of the elements. Finally, the proposed methodology is applied to real aerial orthoimages of orchard plots.

Historical Land Use as a Feature for Image Classification

This paper analyzes the effect of the addition of historical land-use as a descriptive feature in plot-based image classification when updating land-use/land-cover geospatial databases. Several historical databases have been simulated to assess the influence and significance of this feature in the classification. The causes, nature, and evolution of classification errors as the database currency varies are analyzed; and the impact of these errors on change detection during the updating process is evaluated. The results show that the addition of historical land-use information increases the overall accuracy of image classifications. During a database updating process, changes are detected by comparing the historical land-use with the classification results. The main drawback of employing historical land-use as a descriptive feature in image classification for change detection is that the percentage of undetectable errors significantly increases as more accurate is the database information.

Change detection in periurban areas based on contextual classification

The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and by the Spanish Instituto Geografico Nacional.

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Abstract Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.

A Technical Solution to Allow Off-line Mobile Map Querying of Discrete and Continuous Geographic Attribute Data.

Abstract In this article, a technique towards the generation of hybrid raster-attributes map for use in mobile devices is described. Our solution is based on coding the map attributes within an image using RGB values. The designed coding method enables the simultaneous storage of discrete thematic attributes and continuous quantitative attributes. This approach offers a wide range of possible uses. Small memory storage requirements and the simplicity of the software enable this coding method to be used efficiently in mobile devices without Internet connection. This article describes the basic fundamentals of the coding technique, as well as the operation and limitations regarding the volume of information. Two specific applications are presented: a topographic map used for recreational activities, and a visitor map of a university campus.

Potential evaluation of different types of images and their combination for the classification of gis objects cropland and grassland

In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of the two most frequent classes: cropland and grassland. For our experiments three categories of imagery, high resolution aerial images, high resolution RapidEye satellite images and medium resolution Disaster Monitoring Constellation (DMC) satellite images are examined. An object-based image classification, as one of the most reliable methods for the automatic updating and evaluation of landuse geospatial databases, is chosen. The object boundaries are taken from a GIS database, each object is described by means of a set of image based features. Spectral, textural and structural (semivariogram derived) features are extracted from images of different dates and sensors. During classification a supervised decision trees generating algorithm is applied. To evaluate the potential of the different images, all possible combinations of the available image data are tested during classification. The results show that the best performance of landuse classification is based on RapidEye data (overall accuracy of 90%), obtaining slightly accuracy increases when this imagery is combined with additional image data (overall accuracy of 92%). * Corresponding author.