Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

International audience—Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that at the present can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators (e.g., attribute filters) have proven their effectiveness in processing very high resolution images. Attribute filters are based on attributes which can be efficiently implemented on tree-based image representations. In this work, we considered the definition of min, max, direct and subtractive filter rules for the computation of attribute filters over the tree of shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas

Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.Peer Reviewe

Consulting Services to Determine the Effectiveness of Vegetation Classification Using WorldView 2 Satellite Data for the Greater Everglades

The purpose of this project was to evaluate the use of remote sensing 1) to detect and map Everglades wetland plant communities at different scales; and 2) to compare map products delineated and resampled at various scales with the intent to quantify and describe the quantitative and qualitative differences between such products. We evaluated data provided by Digital Globe’s WorldView 2 (WV2) sensor with a spatial resolution of 2m and data from Landsat’s Thematic and Enhanced Thematic Mapper (TM and ETM+) sensors with a spatial resolution of 30m. We were also interested in the comparability and scalability of products derived from these data sources. The adequacy of each data set to map wetland plant communities was evaluated utilizing two metrics: 1) model-based accuracy estimates of the classification procedures; and 2) design-based post-classification accuracy estimates of derived maps

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

International audience—In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by non-parametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines (SVMs) and Random Forest (RF) are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended attribute profiles (EAPs)