Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation

Recent years have witnessed an increased interest towards compression-based methods and their applications to remote sensing, as these have a data-driven and parameter-free approach and can be thus succesfully employed in several applications, especially in image information mining. This paper expands the algorithmic information theory frame, on which these methods are based. On the one hand, algorithms originally defined in the pattern matching domain are reformulated, allowing a better understanding of the available compression-based tools for remote sensing applications. On the other hand, the use of existing compression algorithms is proposed to store satellite images with added semantic value

A fast compression-based similarity measure with applications to content-based image retrieval

Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the complexity of these methods, without degradations in performance. On its basis a content-based color image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant color features. Through the FCD a better understanding of compression-based techniques is achieved, by performing experiments on datasets which are larger than the ones analyzed so far in literature

Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing

Shadows are frequently observable in high-resolution images, raising challenges in image interpretation, such as classification and object detection. In this paper, we propose a novel framework for shadow detection and restoration of atmospherically corrected hyperspectral images based on nonlinear spectral unmixing. The mixture model is applied pixel-wise as a nonlinear combination of endmembers related to both pure sunlit and shadowed spectra, where the former are manually selected from scenes and the latter are derived from sunlit spectra following physical assumptions. Shadowed pixels are restored by simulating their exposure to sunlight through a combination of sunlit endmembers spectra, weighted by abundance values. The proposed framework is demonstrated on real airborne hyperspectral images. A comprehensive assessment of the restored images is carried out both visually and quantitatively. With respect to binary shadow masks, our framework can produce soft shadow detection results, keeping the natural transition of illumination conditions on shadow boundaries. Our results show that the framework can effectively detect shadows and restore information in shadowed regions

Improving the Classification in Shadowed Areas using Nonlinear Spectral Unmixing

This paper presents a shadow restoration method based on the nonlinear mixture model. A shadowed spectrum is modeled by using a pure sunlit spectrum for the same material following physical assumptions. Regarding pure sunlit and shadowed spectra as endmembers, an unmixing process is then conducted pixel-wise using a nonlinear mixture model. Shadow pixels are restored by simulating their exposure to sunlight through a combination of selected sunlit endmembers spectra, weighted by abundance values. Experiments conducted on a real airborne hyperspectral image are eval- uated through spectra comparison and classification. In addition, a soft shadow map is generated, which quantifies the shadow intensity at the edges between sunlit and shadow areas

Solar Panels Area Estimation Using the Spaceborne Imaging Spectrometer DESIS: Outperforming Multispectral Sensors

Solar photovoltaic power plants are in rapid expansion throughout the world, with the total area occupied by panels being linked to the total electrical power produced. This paper considers this case as an instance of the generic problem of estimating the total area occupied by a class of interest in spaceborne hyperspectral images. As the spatial resolution characterizing these sensors is too coarse, spectral unmixing techniques identify the contribution of a specific material to the spectrum related to a single image element. Final results are obtained by summing all contributions in an area of interest, and favourably compared to pixel-based detection, also using higher resolution Sentinel-2 data. The data used in this paper are acquired by the currently operative DESIS sensor, mounted on the International Space Station, encouraging the use of spaceborne imaging spectrometers for such applications

Feasibility of DESIS Imaging Spectrometer for the Detection of Burned Areas: The Case Study of Arakapas Fire in Cyprus 2021

This paper presents an assessment of the use of the DESIS sensor, the imaging spectrometer mounted on the International Space Station (ISS), for the detection of burned areas in sensitive areas. Each DESIS acquisition records continuous spectral information over areas of 30 km × 30 km, a suitable size for such applications, in the visible and near infrared ranges across 235 spectral bands. As DESIS is the first hyperspectral sensor allowing rapid revisit of any site of interest excluding extreme high latitudes, pre- and post-event images can be available, where burned areas can be detected with change detection techniques coupled with suitable, narrow-band spectral indices. Such products may help in timely raising awareness on the endangerment of cultural and natural heritage sites and landscapes, emphasising the importance of Earth Observation (EO) data for monitoring, digitizing and documenting valuable cultural heritage sites. A first assessment for the case of the Arakapas fire in Cyprus is presented. This event started on Saturday, the 3rd of July 2021 in the Limassol district near the village of Arakapas and was controlled after approximately 24 hours. The area affected by the fire is designated as an area of special aesthetic value of the Troodos mountain range to the South West Shores and is included in the Troodos UNESCO global geo-park, which characterizes it as a natural heritage landscape. According to the Department of Antiquities, there are 13 cultural heritage sites in the extended region of the fire. Indeed, several churches of significant cultural value were in danger, being located close to the fire. DESIS acquisitions in cloud-free conditions are available for the pre- and post-event dates of the 10th of June and 31st of July 2021, respectively. The difference of the narrow-band Normalized Differential Vegetation Index (NDVI), using the narrow bands centered around 620 and 700 nm respectively was used to identify the burned area. Results are favourably matched to available coordinates of known burned sites, and the affected area looks overall well identified according to the available information on the event. Short wave infrared (SWIR) information is usually characterized by relevant emissions in presence of fires and widely used for this kind of analysis. Nevertheless, results show that DESIS data yield precise burnt area maps, in spite of the lack of this spectral information. Also 10 spectral bands of multispectral Sentinel-2 images from the 12th of June and 27th of July, with spatial resolution between 10 m and 20 m and a swath width of 290 km, were used to calculate different indices frequently applied for burned area assessment using EO data, such as the Normalised Burn Ratio (NBR), Burned Area Index (BAI), and dNBR (differential NBR) Results from these broadband indices are accurate, and are subsequently compared to the results of the narrowband outcomes from DESIS

Enhancing risk assessment and monitoring for cultural heritage sites through data cubes: a multidimensional approach

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. Data cubes can be regarded as a three-dimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, - in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks

Shadow-Aware Nonlinear Spectral Unmixing for Hyperspectral Imagery

In hyperspectral imagery, differences in ground surface structures cause a large variation in the optical scattering in sunlit and (partly) shadowed pixels. The complexity of the scene demands a general spectral mixture model that can adapt to the different scenarios of the ground surface. In this paper, we propose a physics-based spectral mixture model, i.e., the extended shadow multilinear mixing (ESMLM) model that accounts for typical ground scenarios in the presence of shadows and nonlinear optical effects, by considering multiple illumination sources. Specifically, the diffuse solar illumination alters as the wavelength changes, requiring a wavelength-dependent modeling of shadows. Moreover, we allow different types of nonlinear interactions for different illumination conditions. The proposed model is described in a graph-based representation, which sums up all possible radiation paths initiated by the illumination sources. Physical assumptions are made to simplify the proposed model, resulting in material abundances and four physically interpretable parameters. Additionally, shadow-removed images can be reconstructed. The proposed model is compared with other state-of-the-art models using one synthetic dataset and two real datasets. Experimental results show that the ESMLM model performs robustly in various illumination conditions. In addition, the physically interpretable parameters contain valuable information on the scene structures and assist in performing shadow removal that outperforms other state-of-the-art works

Monitoring and Automatic Change Detection of Cultural Heritage Sites using Sentinels and Copernicus Contributing Missions

Currently available very high resolution space borne imagery can be used for mapping and 3D modeling of archaeologic sites and monuments from all over the world. This allows also the continuous monitoring, protection from natural and human threatening and may also be the base for virtual or real reconstruction of monuments. As an example it is shown how a mostly automatic approach for operationally monitoring from space may work on the example of the world heritage site of Palmyra, Syria