Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Integration of points cloud data from airborne and terrestrial laser scanner

The purpose of this study is trying to solve the shortage of the data due to the limitations of the laser scanner instrument in data collection. The data integration between ALS with TLS are able to provide complete data for used by other applications such as 3D model of building, 3D model of forestry mapping, documentation of historical building, forensic and many more. The scope of this study focusing on the scanning the UTM Eco-Home building using ALS and TLS. The UTM Eco-Home building was chosen because it has a unique design and structure that can be test the capabilities of the laser scanner instruments. The ALS used to scan the roof top of the building and the TLS used to scan the façade of the building. The laser scanner instruments are used in data collection are to ensure that is no part of the building are missed to scan. After completing scanning the UTM Eco-Home building process, the dataset used for integration through registration method using man-made. The man-made is chosen because the characteristic of man-made can be seen in both dataset. The level of accuracy is assessed by comparison method with the conventional measurement using total station. The result from the integration is using for many purposes for example 3D city modelling, 3D forestry mapping, 3D topographic mapping, historical documentation and others

Quantifying the urban forest environment using dense discrete return LiDAR and aerial color imagery for segmentation and object-level biomass assessment

The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized. In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Review of remote sensing for land administration: Origins, debates, and selected cases

Conventionally, land administration—incorporating cadastres and land registration—uses ground-based survey methods. This approach can be traced over millennia. The application of photogrammetry and remote sensing is understood to be far more contemporary, only commencing deeper into the 20th century. This paper seeks to counter this view, contending that these methods are far from recent additions to land administration: successful application dates back much earlier, often complementing ground-based methods. Using now more accessible historical works, made available through archive digitisation, this paper presents an enriched and more complete synthesis of the developments of photogrammetric methods and remote sensing applied to the domain of land administration. Developments from early phototopography and aerial surveys, through to analytical photogrammetric methods, the emergence of satellite remote sensing, digital cameras, and latterly lidar surveys, UAVs, and feature extraction are covered. The synthesis illustrates how debates over the benefits of the technique are hardly new. Neither are well-meaning, although oft-flawed, comparative analyses on criteria relating to time, cost, coverage, and quality. Apart from providing this more holistic view and a timely reminder of previous work, this paper brings contemporary practical value in further demonstrating to land administration practitioners that remote sensing for data capture, and subsequent map production, are an entirely legitimate, if not essential, part of the domain. Contemporary arguments that the tools and approaches do not bring adequate accuracy for land administration purposes are easily countered by the weight of evidence. Indeed, these arguments may be considered to undermine the pragmatism inherent to the surveying discipline, traditionally an essential characteristic of the profession. That said, it is left to land administration practitioners to determine the relevance of these methods for any specific country context. © 2021 by the authors. Licensee MDPI, Basel, Switzerland