Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface

Tracking pedestrians based on visual sensors has many diverse applications, among them autonomous driving. Besides obtaining high recall, maintaining the consistency of tracked trajectories during data association is one of the most crucial issues of any tracker. This issue has been tackled in the literature for some time, taking advantage of geometry cues for improving the pairwise matching of detections

In this paper, a SLAM-integrated MMS with the capability of efficient, consistent and robust mapping in complex environments where GNSS signal is intermittently lost is presented. The system is developed to take the feedback from mapping module to the localization module into account, which significantly improves the smoothness of the 6 DOF trajectory estimation. In particular, the complex mapping

Camera localization is a fundamental requirement in robotics and computer vision. This paper introduces a pose-to-image translation framework to tackle the camera localization problem. We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation. PoseGANs feature a number of innovations including a distance metric based

Globally, flooding is the leading cause of natural disaster related deaths, especially in Bangladesh where approximately one third of national area gets flooded annually by overflowing rivers during the monsoon season, which drastically affects paddy rice agriculture and food security. However, existing studies on the pattern of floods and their impact on agriculture in Bangladesh are limited. Here

There is a growing need for 3D colored maps acquired from multi-sensor moving platforms. Accurate multi-sensor data alignment is an important prerequisite for the construction of 3D colored maps derived from simultaneously acquired camera and Light Detection and Ranging (LiDAR) data. However, current alignment methods are hampered by low automation, heavy computational costs or tedious system calibration

Image-based and range-based solutions can be used for the acquisition of valuable data in medicine. However, most of these methods are not valid for non-static patients. Cranial deformation is a problem with high prevalence among infants and image-based solutions can be used to assess the degree of deformation and monitor the evolution of patients. However, it is required to deal with infants normal

Multispectral LiDAR (Light Detection And Ranging) is characterized of the completeness and consistency of its spectrum and spatial geometric data, which provides a new data source for land-cover classification. In recent years, the convolutional neural network (CNN), compared with traditional machine learning methods, has made a series of breakthroughs in image classification, object detection, and

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to unsupervised learning to obtain monocular

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the

It is critical to inventory abandoned farmland soon after it is generated, to better manage agricultural resources and to prevent negative consequences that would otherwise follow. This study aims to distinguish abandoned farmlands from active croplands—rice paddy and agricultural fields—by discerning the phenological trajectories over a short-term period of three years (Jan. 2016 to Dec. 2018) in

In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS) pixels are used to estimate geophysical parameters, essentially deformation. Conventionally, PS pixels are selected based on the estimated noise present in the spatially uncorrelated phase component along with look-angle error in a temporal interferometric stack. In this study, two deep learning architectures, namely convolutional

Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide. There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free

This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when

Airborne LiDAR point cloud classification has been a long-standing problem in photogrammetry and remote sensing. Early efforts either combine hand-crafted feature engineering with machine learning-based classification models or leverage the power of conventional convolutional neural networks (CNNs) on projected feature images. Recent proposed deep learning-based methods tend to develop new convolution

A major challenge in the application of state-of-the-art deep learning methods to the classification of mobile lidar data is the lack of sufficient training samples for different object categories. The transfer learning technique based on pre-trained networks, which is widely used in deep learning for image classification, is not directly applicable to point clouds, because pre-trained networks trained

Advancement in Remote Sensing allows rapid mangrove mapping without the need for data-intensive methodologies, complex classifiers, and skill-dependent classification techniques. This study proposes a new index, the Mangrove Vegetation Index (MVI), to rapidly and accurately map mangroves extent from remotely-sensed imageries. The MVI utilizes three Sentinel-2 bands green, Near Infrared (NIR) and Shortwave

Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated

Terrestrial laser scanning (TLS) has been recognized as an accurate means for non-destructively deriving three-dimensional (3D) forest structural attributes. These attributes include but are not limited to tree height, diameter at breast height, and leaf area density. As such, TLS has become an increasingly important technique in forest inventory practices and forest ecosystem studies. Multiple TLS

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models,

Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30–50 cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data. State-of-the-art reconstruction methods typically generate 2.5D elevation data. Here, we present an approach to recover full 3D surface meshes from multi-view satellite imagery. The

Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing

Faced with the problem of the large scale variation, geospatial object detection in multiple spatial resolution (MSR) remote sensing imagery is a challenging task. To avoid the scale problem, the current convolutional neural network (CNN) based object detectors use multi-scale structures in the convolutional layer level to improve the detection performance by utilizing different receptive fields in

Satellite-based retrieval has become a popular PM2.5 monitoring method. To improve the retrieval performance, multiple variables are usually introduced as auxiliary variables, in addition to aerosol optical depth (AOD). The different kinds of variables are usually at different resolutions, varying from sub-kilometer to dozens of kilometers. Generally speaking, when undertaking the retrieval, the variables

The existence of several unnecessary and highly correlated coefficients in the rational function model (RFM), known as rational polynomial coefficients (RPCs), leads to an ill-posed design matrix. Under such a condition, any small perturbation, including errors in the problem’s input, causes drastic changes in the final results. The ill-posedness of the design matrix, intrinsic to the RFM problem,

By utilizing the airborne laser scanning (ALS) and terrestrial laser scanning (TLS), the land surface information from both top view and side view can be captured rapidly. However, due to the different perspective views, resolutions, and ranges, the automatic localization of multiple TLS scans to ALS is challenging. To address this issue, this paper proposes a novel skyline context-based method. First

Persistent Scatterer Interferometry (PSI) is a time series remote sensing technique to estimate displacements of geo-objects from the interferometric phases of selected Persistent Scatterers (PS). The relative position of a scatterer within a resolution cell causes an additional phase contribution in the observed phase, which needs to be accounted for in PSI processing. Here we analyze the influence

Coastal lagoon–tidal inlet systems occur worldwide, and each has its own unique evolution characteristics in relation to its geographical location, sediment characteristics, and tidal current and ocean wave conditions. However, insufficient observation data means that it is often difficult to fully understand the long-term and short-term evolution of ebb-tidal deltas, and it is even more difficult

Three-dimensional reconstruction of line and plane structures from two images is a major task in urban building modeling. However, traditional line segment (LS) matching methods frequently produce inaccurate few LS matches, and further lead to unreliable sparse 3D line-plane reconstruction. To address these issues, this paper presents an effective line-plane reconstruction method based on angle regularization

Rapid detection of 3D objects in indoor environments is essential for indoor mapping and modeling, robotic perception and localization, and building reconstruction. 3D point clouds acquired by a low-cost RGB-D camera have become one of the most commonly used data sources for 3D indoor mapping. However, due to the sparse surface, empty object center, and various scales of point cloud objects, 3D bounding

In situ remote sensing reflectance (Rrs) measured using the above-water approach usually suffers from residual reflected skylight (Δ), and thus its applicability is limited. In this study, we proposed a simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs spectrum. The performance of the proposed method was evaluated using both

Airborne hyperspectral remote sensing is an important application in the ecological monitoring of the environment in mining areas, and accurate preprocessing of the original images is the key to quantitative information retrieval. The original image data need radiation correction to acquire surface reflectance data. Due to the impact of the field angle, incidental radiance, and the bidirectional reflectance

In Mediterranean fire-prone ecosystems, shifts in fire regime as a consequence of global change could modify the resilience of vegetation communities. In this paper, we aim to compare the efficiency of high and moderate spatial resolution satellite imagery in the evaluation of resilience in a fire-prone landscape under different fire regime categories using two pixel unmixing techniques. A time series

The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire

Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure;

The noise pollution issue seriously obstructs subsequent interpretation and application of the hyperspectral image (HSI). In this work, differing from most existing HSI denoising methods ideally assumed that noise in different bands denotes independent & identically distributed (i.i.d.), we propose a novel HSI denoising approach focusing on non-i.i.d. noise removal. The presented framework collaboratively

As one of the most important features for human perception, contours are widely used in many graphics and mapping applications. However, for large outdoor scale point clouds, contour extraction is considerably challenging due to the huge, unstructured and irregular point space, thus leading to massive failure for existing approaches. In this paper, to generate contours consistent with human perception

Unmanned Aerial Systems (UAS) represent an important niche platform for measuring vegetation health, structure, and productivity; metrics that directly inform sustainable conservation and development initiatives in rural African savannas. Products derived from UAS imagery have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity

The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing

With the development of 3D measurement systems, dense colored point clouds are increasingly available. However, up to now, their use in interactive applications has been restricted by the lack of support for point clouds in game engines. In addition, many of the existing applications for point clouds lack the capacity for fluent user interaction and application development. In this paper, we present

For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to

This publication presents a procedure for the determination of all three components of crack opening vectors from stereoscopic image sequences of a specimen under load in civil engineering material testing. The method is based on analyzing stereoscopic image sequences of a concrete specimen with a surface texture, which is suitable for applying image matching techniques. Spatio-temporal correspondences

This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China

Deep learning based methods are the state-of-the-art in panchromatic (PAN)/multispectral (MS) fusion (which is generally called “pansharpening”). In this paper, to solve the problem of the insufficient spatial enhancement in most of the existing deep learning based pansharpening methods, we propose a novel pansharpening method based on a residual convolutional neural network (RCNN). Differing from

With the development of three-dimensional (3D) point cloud acquisition technologies, supervoxels have become increasingly important, as they provide compact and uniform representations. In this study, a novel supervoxel segmentation method, supervoxels based on local allocation (SVLA), is proposed. SVLA is composed of three steps, namely extreme point determination, local allocation (LA), and connectivity