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Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth’s surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that can be reached when analyzing a given data set. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this article how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach. [ABSTRACT FROM AUTHOR]

A method based on spectral unmixing (SU) for snow cover estimation from a time series of optical images is proposed in this letter. Specifically, we have developed an endmember estimation procedure that exploits the temporal continuity of a scene. Consecutive dates are jointly processed for a more precise description of background materials improving the estimation of a fractional snow cover map. The proposed workflow relies on up to three consecutive acquisitions over the same area to extract an appropriate set of background spectra to be considered as endmembers. In greater details, multiple sets of endmembers are extracted from different images in the time series by a geometrical automated endmember extraction algorithm and the most relevant one is selected in terms of reconstruction error. Snow cover maps are then estimated by SU considering as endmembers the snow spectra coming from a spectral library and those associated with the background materials as estimated by the proposed procedure. The proposed technique is quantitatively validated considering Moderate-Resolution Imaging Spectroradiometer Terra data over the French Alps and Moroccan High Atlas image time series and comparing the estimated snow cover maps with high-resolution reference data. The experiment clearly demonstrates the effectiveness of the generated set of endmembers using three different approaches to abundance estimation. [ABSTRACT FROM AUTHOR]

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 (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of \min, \max, \textdirect, and \textsubtractive filter rules for the computation of AFs 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. [ABSTRACT FROM AUTHOR]

Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a powerful tool able to model spatial information (e.g., contextual relations) of the image. However, due to the shortcomings of using the morphological profiles, many variants, extensions, and refinements of its definition have appeared stating that the morphological profile is still under continuous development. In this case, recently introduced theoretically sound attribute profiles (APs) can be considered as a generalization of the morphological profile, which is a powerful tool to model spatial information existing in the scene. Although the concept of the AP has been introduced in remote sensing only recently, an extensive literature on its use in different applications and on different types of data has appeared. To that end, the great amount of contributions in the literature that address the application of the AP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, and hyperspectral) proves how the AP is an effective and modern tool. The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works. [ABSTRACT FROM AUTHOR]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI) and spectral unmixing (SU). These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product) for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow) at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates), the Pyrenees (30 dates), and the Moroccan Atlas (24 dates). This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version of MOD10A1 (Collection 6) compared to the older version (Collection 5) are significant. Products based on SU provide a good alternative and more accurate retrieval of the snow fraction where wider ranges of land covers are concerned. The fusion process and its resulting 250 m spatial resolution product improve snow line retrieval. False detection in mixed pixels, probably due to the spectral variability associated with the various materials in the spectral mixture, has been identified as an area that will require improvement. [ABSTRACT FROM AUTHOR]

The HYEP project (ANR HYEP 14-CE22-0016-01 Hyperspectral imagery for Environmental urban Planning - Mobility and Urban Systems Programme - 2014) confirmed the interest of a global approach to the urban environment by remote sensing and in particular by using hyperspectral imaging (HI). The interest of hyperspectral images lies in the range of information provided over wavelengths from 0.4 to 4 μm; they thus provide access to spectral quantities of interest and to chemical or biophysical parameters of the surface. HYEP's objective was to specify this and to propose a panel of methods and treatments taking into account the characteristics of other existing sensors in order to compare their performance. The developments carried out were applied and evaluated on hyperspectral airborne images acquired in Toulouse and Kaunas (Lithuania), also used to synthesize space systems: Sentinel-2, Hypxim/Biodiversity and Pleiades. Among the locks identified were those related to improving the spatial capabilities of the sensors and spatial scale changes, which were partially overcome by fusion and sharpening approaches, which proved to be successful. After a description of our hyperspectral data set acquired over Toulouse, an analysis is conducted on several existing and accessible spectral databases. Then, the chosen methods are presented. They include extraction, fusion and classification methods, which are then applied on our dataset synthetized at different spatial resolution to evaluate the benefits and the complementarity of hyperspectral imagery in comparison with other traditional sensors. Some specific applications are investigated of interest for urban planners: impervious soil map, vegetation species cartography and detection of solar panels. Finally, discussion and perspectives are presented. [ABSTRACT FROM AUTHOR]

Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post-processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas. [ABSTRACT FROM AUTHOR]

Effective strategies for forest characterization and monitoring, are important to support sustainable management. Recent advances in remote sensing, like LiDAR and hyperspectral sensors, provide valuable information to characterize forests at stand, plot and tree level. LiDAR data offers potentialities for analyzing structural properties of canopy, while hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits. The fusion of these two modalities provide better and more robust estimation of the forestry variables. This work presents a review of methods for the integration of LiDAR data and hyperspectral imagery by taking into account applications related to forestry. Although different authors propose different approaches of data fusion, our review is divided according to three levels of fusion based on data processing: observation level, feature level, and decision level. Fusion at observation level preserves most of the original information from both modalities by integrating 3D point cloud with the hyperspectral information. One alternative for this task is the generation of the Canopy Height Model (CHM), which is the most used two-dimensional representation of LiDAR data. Fusion at feature level seeks to complement information by exploiting the original data. The most relevant features extracted from LiDAR or hyperspectral data are statistical, structural, topographic, vegetation indices, textural and dimension reduction. Some of these feature descriptors are stacked to be fused at higher level, or these are normalized to be integrated through methods of dimension reduction or feature selection. Fusion at decision level is directly associated to the forestry application and implies tasks of segmentation, classification, data association and prediction - estimation. This review describes relationship between the three levels of fusion and the methods used in each considered approach. The most important applications are oriented to species mapping, functional and physiological attributes, structural attributes, above ground biomass and carbon density and landcover maps. [ABSTRACT FROM AUTHOR]