Successive re-calibrations were implemented in Landsat-8 TIRS data since launch. This paper evaluates the performances of both: (1) these re-calibrations, up to the last calibration update announced for TIRS data in the next Landsat Collection 2; and (2) single-channel (SC) corrections and split-window (SW) algorithms to retrieve land surface temperature (LST) from TIRS data. A robust and accurate

Multisensor Earth observation has significantly accelerated the development of multisensor collaborative remote sensing applications such as all-weather mapping using synthetic aperture radar (SAR) images and optical images. However, in the real-world application scenarios, not all data sources may be available, namely, the missing-modality problem, e.g., the poor imaging conditions obstruct the optical

Remote monitoring of chlorophyll-a (chla) has been widely used to evaluate the trophic state of inland and coastal waters, however, there is still much uncertainty in the algorithms applied in different optical water types. The influence of different atmospheric correction (AC) processors, which can also provide correction for sunglint and adjacency effects, on the retrieved chla is poorly understood

As a dynamic and essential component in the road environment of urban scenarios, vehicles are the most popular investigation targets. To monitor their behavior and extract their geometric characteristics, an accurate and instant measurement of vehicles plays a vital role in traffic and transportation fields. Point clouds acquired from the mobile laser scanning (MLS) system deliver 3D information of

With the development of photon-counting sensors, spaceborne photon-counting lidars have shown many advantages in mapping underwater topography. Although a space based lidar is normally a profiling system, the depth penetration and the vertical accuracy achieved with a bathymetric lidar is superior to imagery (that only provides relative depths). Therefore, many satellite derived bathymetry products

Identifying spatial distribution of crop planting in large-scale is one of the most significant applications of remote sensing imagery. As an active remote sensing system, synthetic aperture radar (SAR) provides high-resolution polarimetric information of land covers. Nowadays, it is possible to carry out continuous multi-temporal analysis of crops in large-scales since an increased number of spaceborne

Monitoring wetlands is necessary in order to understand and protect their ecohydrological balance. In Ireland, traditionally wetland-monitoring is carried out by manual field visits which can be very time-consuming. To automate the process, this study extends the ability of remote sensing-based monitoring of wetlands by combining RGB image processing, machine learning algorithms, and satellite data

Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages

Detecting the changes of buildings in urban environments is essential. Existing methods that use only nadir images suffer from severe problems of ambiguous features and occlusions between buildings and other regions. Furthermore, buildings in urban environments vary significantly in scale, which leads to performance issues when using single-scale features. To solve these issues, this paper proposes

Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked.

Zanthoxylum bungeanum Maxim (ZBM) is an important woody species in large parts of Asia, which provides oils and medicinal materials. Timely and accurate mapping of its spatial distribution and planting area is of great significance to local economy and ecology. As a special tree species planted in the Grain for Green Program of China, Linxia Hui Autonomous Prefecture (Linxia) in Gansu Province of China

Over the last few decades, the methods of global image orientation, which is also called global SfM, have attracted a lot of attention from researchers, mainly thanks to its advantage of time efficiency. Based on the input of relative orientation results, most conventional global SfM methods employ a two-step strategy consisting of global rotation estimation and global translation estimation. This

Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering

Hyperspectral remote sensing can get spatially and spectrally continuous data simultaneously. However, the imaging equipment is usually expensive and complex, along with the low spatial resolution. In recent years, reconstruction of hyperspectral image by deep learning from the widely used low-cost, high spatial resolution RGB camera, has attracted extensive attention in many fields. However, most

The advances in remote sensing sensors during the last two decades have led to the production of very high spatial resolution multispectral images. In order to adapt to this rapid development and handle these data, object-based analysis has emerged. A critical part of such an analysis is image segmentation. The selection of optimal segmentation parameters' values generates a qualitative segmentation

Phenology plays an important role in revealing the spatiotemporal evolution of forest ecosystem carbon cycles. The accuracy of vegetation phenology estimates based on remote sensing has improved in temperate zones. However, subtropical vegetation is complex, and the corresponding phenology estimates using remote sensing face great challenges. Bamboo forests are subtropical unique forest types and exhibit

The increasing availability of both indoor positioning services and sensors for 3D data capture, such as RGB-D sensors, allows the provision of indoor spatial information services for indoor localization-based applications. To efficiently realize these services, the indoor information and the relationships between indoor spaces are required. The recently released Indoor Geography Markup Language (IndoorGML)

Sodicity is a major soil constraint in many arid and semi-arid regions worldwide, including Australia, which adversely affects the ability of crops to take up water and nutrients from the soil, reducing yield. Reliable methods and tools are required for appropriate selection of traits, may provide a better understanding of crop responses to multiple stresses, especially in sodic soil. A novel strategy

The measurement of surface moisture on beaches is vital for studying aeolian sand transport mechanisms, but existing techniques are not adequate for monitoring the surface moisture dynamics over a substantial beach section. In this study, we investigated the suitability of a new remote sensing method to monitor the spatiotemporal variation in surface moisture on a sandy beach using a long-range static

Consistent global color correction across multiple-view images in three-dimensional (3D) reconstruction is an important and challenging problem. The present work addresses this issue by proposing a novel global color correction method for multi-view images based on a spline curve remapping function. In contrast to existing methods, we obtain a series of optimal functions by minimizing the variance

Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different

Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process

The Planet SkySat-C SmallSat constellation can acquire very high resolution (0.7 m to 0.9 m) triplet stereo and video imagery with short revisit times, providing an exciting opportunity for global, on-demand 3D mapping of dynamic surface features. However, a lack of suitable processing software, limited geolocation accuracy, and scene-to-scene offsets currently limit the potential for accurate SkySat

The goal of urban building mesh simplification is to generate a compact representation of a building from a given mesh. Local smoothness and sharp contours of urban buildings are important features for converting unstructured data into solid models, which should be preserved during the simplification. In this paper, we present a general method to filter and simplify 3D building mesh models, capable

For both the positive economic benefit and the negative ecological impact of the rapid expansion of oil palm plantations in tropical developing countries, it is significant to achieve accurate detection for oil palm trees in large-scale areas. Especially, growing status observation and smart oil palm plantation management enabled by such accurate detections would improve plantation planning, oil palm

Salt marshes provide crucial ecological functions and services and are experiencing rapid losses and degradation under global climate changes and high-intensity human activities in coastal zone. However, mapping salt marsh distributions and compositions with high accuracy on the national or global scale remains challenging. Here, we used Sentinel-1 time-series data and knowledge-based automatic decision

Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from

Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate

In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in

Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with

Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions

Pansharpening aims to fuse low-resolution multi-spectral image and high-resolution panchromatic (PAN) image to produce a high-resolution multi-spectral (HRMS) image. In this paper, a new residual learning network based on gradient transformation prior, termed as GTP-PNet, is proposed to generate the high-quality HRMS image with accurate spectral distribution as well as reasonable spatial structure

LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an accurate relative pose). In this work, we develop a novel end-to-end, deep multi-task network that simultaneously

Single tree detection has been a major research topic when it comes to support of collecting automatic field inventory using lidar. All previous methods show under- and over-segmentation effects because the associated control parameters have a limited scope. This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection

A big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense

The scene classification approaches using deep learning have been the subject of much attention for remote sensing imagery. However, most deep learning networks have been constructed with a fixed architecture for natural image processing, and they are difficult to apply directly to remote sensing images, due to the more complex geometric structural features. Thus, there is an urgent need for automatic

Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact

Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To

Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can

We present a comprehensive methodological framework for structural deformation monitoring of critical infrastructure assets based on differential SAR interferometry. By employing persistent scatterer interferometry, deformation time series in line-of-sight are derived from freely available Sentinel-1 single look complex products. These raw time series are analysed and refined using an extensive post-processing

Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive,

Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms

Surface water plays a vital role in natural environment and human development. The research of water extraction method using remote sensing image is a hot topic, which has been widely developed in water index, classification, subpixel, and other aspects. Compared with other methods, a water-index based method has the advantages of fast speed and convenience. The characteristics of surface water, such

This paper presents a novel method for matching line segments in images based on pair-wise geometric constraints and matching redundancy. In this study, pairs of line segments satisfying angle and distance constraints are used as matching primitives. To ensure that each extracted line segment is paired with another line segment, the search region of each line segment is gradually grown until it is

Given the importance of accuracy in land cover (LC) maps, several methods have been adopted to predict per-pixel land cover accuracy (PLCA) of classified remote sensing images. Such a PLCA map provides spatially-explicit accuracy information and is of paramount importance for both producers and end-users of LC maps to thoroughly understand the spatial distribution of accuracy. In this study, we proposed

Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a

Alongside spatial information, an intensity scalar is also measured by LiDAR (Light Detection And Ranging) sensor systems. It corresponds to the amplitude of the backscattered laser beam after reflection on the scanned surface. This information isn’t directly usable due to dependencies of geometrical parameters occurring during the scanning process and additional processing modifications. The research

Point cloud registration is invariably an essential and challenging task in the fields of photogrammetry and computer vision to align multiple point clouds to a united reference frame. In this paper, we propose a novel global registration method using a robust phase correlation method for registration of low-overlapping point clouds, which is less sensitive to noise and outliers than feature-based

Vegetation phenology is considered an important biological indicator in understanding the behaviour of ecosystems and how it responds to environmental cues. Changes in vegetation dynamics have been strongly linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems, such as the land surface-atmosphere exchange of water and carbon, energy

Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally

Highly accurate 2D maps can supply basic geospatial information for efficient and accurate indoor building modeling. However, problematic scenarios, which are characterized by few features, similar components and large scales, seriously influence data association and cumulative error elimination, and thus degrade simultaneous localization and mapping (SLAM)-based mapping quality. In this paper, a cross-correction

Fruit cluster characteristics such as compactness, maturity, berry number, and berry size, are important phenotypic traits associated with harvestability and yield of blueberry genotypes and can be used to monitor berry development and improve crop management. The goal of this study was to develop a complete framework of 3D segmentation for individual blueberries as they develop in clusters and to

Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when dealing with VHR images with large-scale missing regions, existing spatial-based methods often destroy the structural information of ground objects. To alleviate this