Anna Brook

Automatic Registration of Airborne and Spaceborne Images by Topology Map Matching with SURF Processor Algorithm

Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi‑sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image‑processing methods (SURF—Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor.

Modelling and monitoring urban built environment via multi-source integrated and fused remote sensing data

Investigation of urban built environment includes a wide range of applications that require 3-D information. New approaches are needed for near-real-time analysis of the urban environment with natural 3-D visualisation of extensive coverage. Hyperspectral remote sensing technology is a promising and powerful tool to assess quantitative classification of urban materials by exploring possible chemical/physical changes using spectral information across the VIS-NIR-SWIR spectral region. Light Detection And Ranging (LiDAR) technology offers precise information about the geometrical properties of the surfaces and can reflect the different shapes and formations in the complex urban environment. Generating a monitoring system that is based on integrative fusion of hyperspectral and LiDAR data may enlarge the application envelope of each individual technology and contribute valuable information on urban built environments and planning. A fusion process defined by a data-registration algorithm and including spectral/spatial and 3-D information is developed and presented. The proposed practical 3-D urban environment application for photogrammetric and urban planning purposes integrates the hyperspectral (spectrometer, ground camera and airborne sensor) and LiDAR data. This application may provide urban planners, civil engineers and decision-makers with tools to consider quantitative spectral information in the 3-D urban space.

Fusion of hyperspectral images and LiDAR data for civil engineering structure monitoring

Investigation of civil engineering materials includes a wide range of applications that requires three-dimensional (3D) information. Complex structures shapes and formations within heterogeneous artificial/natural land covers under varying environmental conditions requires knowledge on the 3D status of the urban materials for better (visual) interpretation of polluted sources. Obtaining 3D information and merge them with aerial photography is not a trivial task. It is thus, strongly needed to develop new approaches for near real time analysis of the urban environment with natural 3D visualization of extensive coverage. The hyperspectral remote sensing (HRS) technology is a promising and powerful tool to assess degradation of urban materials in artificial structures by exploring possible chemical physical changes using spectral information across the VIS-NIR-SWIR spectral region (400–2500nm). This technique provides the ability for easy, rapid and accurate in situ assessment of many materials on a spatial domain within near real time condition and high temporal resolution. LiDAR technology, on the other hand, offers precise information about the geometrical properties of the surfaces within the study areas and can reflect different shapes and formations of the complex urban environment. Generating a monitoring system that is based on the integrative fusion between HRS and LiDAR data may enlarge the application envelop of each technology separately and contribute valuable information on urban runoff and planning. The aim of the presented research is to implement this direction and define set of rules for practical integration between the two datasets. A fusion process defined by integrative decision tree analysis includes spectral/spatial and 3D information is developed and presented.

Ash-soil interface: Mineralogical composition and physical structure

Fires exert many changes on the physical, chemical, morphological, mineralogical, and biological properties of soil that, in turn, affect the soils hydrology and nutrient flux, modifying its ability to support vegetation and resist erosion. The ash produced by forest fires is a complex mixture composed of organic and inorganic particles with varied properties. This research was conducted to study and characterized ash properties produced at different temperatures and with different soil organic matter combinations. The samples, which included two treatments of soils with underlying mixed leaves and branches composed mainly by Pinus halepensis, Pistacia lentiscus, Cistus salviifolius and typical herbaceous vegetation, versus samples of mixed leaves and branches alone. Both were exposed to 400°C and 600°C heat in a muffle furnace for 2h. The residue ash was generally grayish, consisting of mixed-sized particles that preserved almost none of the original characteristics of the fuel, and was deposited in ash layers with diverse physicochemical and textural properties. The results of this study highlight the differences between all examined samples and strongly support the assumption that ash produced from a complex vegetation-soil system is a new substance with unique structural, textural, and mineralogical properties. Moreover, the ash produced at different temperatures appeared in distinct layering patterns.

Supervised Vicarious Calibration (SVC) of Multi-Source Hyperspectral Remote-Sensing Data

Introduced in 2011, the supervised vicarious calibration (SVC) approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol for its practical execution has been established—along with possible limitations encountered during the campaign. The technique was applied to multi-sourced HRS data in order to: (1) verify the at-sensor radiometric calibration and (2) obtain radiometric and atmospheric correction coefficients. Spanning two select study sites along the southeast coast of France, data were collected simultaneously by three airborne sensors (AisaDUAL, AHS and CASI-1500i) aboard two aircrafts (CASA of National Institute for Aerospace Technology INTA ES and DORNIER 228 of NERC-ARSF Centre UK). The SVC ground calibration site was assembled along sand dunes near Montpellier and the thematic data were acquired from other areas in the south of France (Salon-de-Provence, Marseille, Avignon and Montpellier) on 28 October 2010 between 12:00 and 16:00 UTC. The results of this study confirm that the SVC method enables reliable inspection and, if necessary, in-situ fine radiometric recalibration of airborne hyperspectral data. Independent of sensor or platform quality, the SVC approach allows users to improve at-sensor data to obtain more accurate physical units and subsequently improved reflectance information. Flight direction was found to be important, whereas the flight altitude posed very low impact. The numerous rules and major outcomes of this experiment enable a new standard of atmospherically corrected data based on better radiometric output. Future research should examine the potential of SVC to be applied to super-and-hyperspectral data obtained from on-orbit sensors.

Three-dimensional wavelets-based denoising of hyperspectral imagery

Abstract. We propose a three-dimensional (3-D) denoising approach and coding scheme. The suggested denoising algorithm is taking full advantage of the supplied volumetric data by decomposing the original hyperspectral imagery into individual subspaces, applying an orthogonal isotropic 3-D divergence-free wavelet transformation. The delineated capability of hierarchically structured wavelet coefficients improves the efficiency of the suggested denoising algorithm and effectively preserves the finest details and the relevant image features by emphasizing a nonlocal similarity and spectral-spatial structure of hyperspectral imagery into sparse representation. The proposed method is evaluated using spectral angle distance for a ground-truth spectral dataset and by classification accuracies using water quality indices, which are particularly sensitive to the presence of noise. The reported results are based on a real dataset, presenting three different airborne hyperspectral systems: AHS, CASI-1500i, and AisaEAGLE. Several qualitative and quantitative evaluation measures are applied to validate the ability of the suggested method for noise reduction and image quality enhancement. Experimental results demonstrate that the proposed denoising algorithm achieves better performance when applied on the suggested wavelet transformation compared with other examined noise reduction and hyperspectral image restoration techniques.

Quantitative Detection of Settled Dust Over Green Canopy Using Sparse Unmixing of Airborne Hyperspectral Data

The main task of environmental and geoscience applications is efficient and accurate quantitative classification of earth surfaces and spatial phenomena. In the past decade, there has been a significant interest in employing hyperspectral unmixing (HU) to retrieve accurate quantitative information latent in hyperspectral imagery data. Recently, the ground-truth and laboratory measured spectral signatures promoted by advanced algorithms are proposed as a new path toward solving the unmixing problem of hyperspectral imagery in semisupervised fashion. This paper suggests that the sensitivity of sparse unmixing techniques provides an ideal approach to extract and identify dust settled over/upon green vegetation canopy using hyperspectral airborne data. Among the available techniques, this study presents the results of seven selected algorithms: 1) non-negative matrix factorization (NMF); 2) L1 sparsity-constrained NMF (L1_NMF); 3) L1/2 sparsity-constrained NMF (L1/2_NMF); 4) graph regularized NMF (G_NMF); 5) structured sparse NMF (SS_NMF); 6) alternating least-square (ALS); and 7) Lins projected gradient (LPG). The performance is evaluated on real hyperspectral imagery data via detailed experimental assessment. The results compared with performances of selected conventional unmixing techniques.

Fusion of Optical and Thermal Imagery and LiDAR Data for Application to 3-D Urban Environment and Structure Monitoring

For many years, panchromatic aerial photographs have been the main source of remote sensing data for detailed inventories of urban areas. Traditionally, building extraction relies mainly on manual photo-interpretation which is an expensive process, especially when a large amount of data must be processed (Ameri, 2000). The characterization of a given object bases on its visible information, such as: shape (external form, outline, or configuration), size, patterns (spatial arrangement of an object into distinctive forms), shadow (indicates the outlines, length, and is useful to measure height, or slopes of the terrain), tone (color or brightness of an object, smoothness of the surface, etc.) (Ridd 1995). Automated assessment of urban surface characteristics has been investigated due to the high costs of visual interpretation. Most of those studies used multispectral satellite imagery of medium to low spatial resolution (Landsat-TM, SPOT-HRV, IRS-LISS, ALI and CHRIS-PROBA) and were based on common image-analysis techniques (e.g. maximum likelihood (ML) classification, principal components analysis (PCA) or spectral indices (Richards and Jia 1999)). The problems of limited spatial resolution over urban areas have been overcome with the wider availability of space-borne systems, which characterized by large swath and high spatial and temporal resolutions (e.g. Worl-View2). However, the limits on spectral information of nonvegetative material render their exact identification difficult. In this regard, the hyperspectral remote sensing (HRS) technology, using data from airborne sensors (e.g. AVIRIS, GER, DAIS, HyMap, AISA-Dual), has opened up a new frontier for surface differentiation of homogeneous material based on spectral characteristics (Heiden et al. 2007). This capability also offers the potential to extract quantitative information on biochemical, geochemical and chemical parameters of the targets in question (Roessner et al. 1998).

Advantages of the Boresight Effect in Hyperspectral Data Analysis

Abstract: Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the VIS-NIR (VNIR) region (400–970 nm) and HAWK for the SWIR region (980–2,450 nm). During the course of more than six years of operating this sensor, we have found that the boresight effect provides a new capacity to analyze hyperspectral data, reported herein. Accordingly, we generated a protocol to use this effect for three applications: (1) enhancing the shadow effect; (2) generating a 3-D view; and (3) better detecting

Spectroscopy as a Diagnostic Tool for Urban Soil

The soils found in urban remnant patches may be considered anthropogenic inner urban soils—soils within the administrative boundaries of a municipalities influenced by activities adding artefacts into the soils. Such activities include housing, trading, traffic, production, and disposing. The objective of this study is to determine the scope to which field spectroscopy methods can be used to extend the knowledge of urban soils features and components. The spectroscopy techniques are used broadly for determining specific components or for differentiating between known ones. Moreover, this technique is able to determine low concentration in various phases and to trace hazardous material, and most studies are keen on quantification of those hazardous. In this paper, a top–down analysis for detecting the presence of minerals, organic matter, and pollutants in mixed soil samples is developed and presented. The developed method applies spectral activity (SA) detection in a structured hierarchical approach to quickly and, more importantly, accurately identify dominant spectral features. The developed method is adopted by multiple in-production tools including continuum removal normalization, guided by polynomial generalization, and spectral-likelihood algorithms: orthogonal subspace projection (OSP) and iterative spectral mixture analysis (ISMA).