Evaluating the Key Finding of Texture Smoothing & Filtering TechniquesÂ

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Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties

Towards quantifying the effects of resource extraction on land cover and topography through remote sensing analysis: Confronting issues of scale and data scarcity

This dissertation focuses on the mapping and monitoring of mineral mining activity using remotely sensed data. More specifically, it explores the challenges and issues associated with remote sensing-based analysis of land use land cover (LULC) and topographic changes in the landscape associated with artisanal and industrial-scale mining. It explores broad themes of image analysis, including evaluation of error in digital elevation models (DEMs), integration of multiple scales and data sources, quantification of change, and remote sensing classification in data-scarce environments. The dissertation comprises three case studies.;The first case study examines the LULC change associated with two scales of mining activity (industrial and artisanal) near Tortiya, Cote d\u27Ivoire. Industrial mining activity was successfully mapped in a regional LULC classification using Landsat multispectral imagery and support vector machines (SVMs). However, mapping artisanal mining required high-resolution imagery to discriminate the small, complex patterns of associated disturbance.;The second case study is an investigation of the potential for quantifying topographic change associated with mountain top removal mining and the associated valley-fill operations for a region in West Virginia, USA, using publicly available DEMs. A 1:24,000 topographic map data, the shuttle radar topography mission (SRTM) DEM, a state-wide photogrammetric DEM, and the Advanced Spaceborne Thermal Emission Radiometer (ASTER) Global DEM (GDEM) were compared to a lidar bare-earth reference DEM. The observed mean error in both the SRTM and GDEM was statistically different than zero and modeled a surface well above the reference DEM surface. Mean error in the other DEMs was lower, and not significantly different than zero. The magnitude of the root mean square error (RMSE) suggests that only topographic change associated with the largest topographic disturbances would be separable from background noise using global DEMS such as the SRTM. Nevertheless, regionally available DEMs from photogrammetric sources allow mapping of mining change and quantification of the total volume of earth removal.;Monitoring topographic change associated with mining is challenging in regions where publicly available DEMs are limited or not available. This challenge is particularly acute for artisanal mining, where the topographic disturbance, though locally important, is unlikely to be detected in global elevation data sets. Therefore, the third and final case study explored the potential for creating fine-spatial resolution bare-earth DEMs from digital surface models (DSMs) using high spatial resolution commercial satellite imagery and subsequent filtering of elevation artifacts using commercial lidar software and other spatial filtering techniques. Leaf-on and leaf-off DSMs were compared to highlight the effect of vegetation on derived bare-earth DEM accuracy. The raw leaf-off DSM was found to have very low error overall, with notably higher error in areas of evergreen vegetation. The raw leaf-on DSM was found to have a RMSE error much higher than the leaf-off data, and similar to that of the SRTM in dense deciduous forest. However, filtering using the commercial techniques developed for lidar notably reduced the error present in the raw DSMs, suggesting that such approaches could help overcome data scarcity in regions where regional or national elevation data sets are not available.;Collectively this research addressed data issues and methodological challenges in the analysis of 3D changes caused by resource extraction. Elevation and optical imagery are key data sets for mapping the disturbance associated with mining. The particular combination required regarding data spatial scale, and for elevation, accuracy, is a function of the type and scale of the mining

Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany

—Large-area urban ecology studies often miss information on vertical parameters of vegetation, even though they represent important constituting properties of complex urban ecosystems. The new globally available digital elevation model (DEM) of the spaceborne TanDEM-X mission has an unprecedented spatial resolution (12 × 12 m) that allows us to derive such relevant information. So far, suitable approaches using a TanDEM-X DEM for the derivation of a normalized canopy model (nCM) are largely absent. Therefore, this paper aims to obtain digital terrain models (DTMs) for the subsequent computation of two nCMs for urban-like vegetation (e.g., street trees) and forest-like vegetation (e.g., parks), respectively, in Berlin, Germany, using a TanDEM-X DEM and a vegetation mask derived from UltraCamX data. Initial comparisons between morphological DTM-filter confirm the superior performance of a novel disaggregated progressive morphological filter (DPMF). For improved assessment of a DTM for urban-like vegetation, a modified DPMF and image enhancement methods were applied. For forest-like vegetation, an interpolation and a weighted DPMF approach were compared. Finally, all DTMs were used for nCM calculation. The nCM for urban-like vegetation revealed a mean height of 4.17 m compared to 9.61 m of a validation nCM. For forest-like vegetation, the mean height for the nCM of the weighted filtering approach (9.16 m) produced the best results (validation nCM: 13.55 m). It is concluded that an nCM from TanDEM-X can capture vegetation heights in their appropriate dimension, which can be beneficial for automated height-related vegetation analysis such as comparisons of vegetation carbon storage between several cities