ABSTRACT Personal laser scanning (PLS) has shown great potential in diameter of breast height (DBH) estimation. Compared with the current laser scanning technique, PLS is hardly restricted by occlusion and trafficability, and produces omnidirectional point cloud data. However, the DBH estimation accuracy using PLS data was not higher in the previous research. One of the leading sources of bias is overlapping

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) is important for maize growth and yield estimations. Light detection and ranging (LiDAR)-derived canopy vertical structural and hyperspectral image-derived vegetation spectral information are complementary for vegetation fPAR estimation. This study explores the potential of artificial neural networks (ANNs) with

Semantic segmentation models based on deep learning have shown remarkable performance in road extraction from high-resolution aerial images. However, it is still a difficult task to segment multiscale roads with high completeness and accuracy from complex backgrounds. To deal with this problem, this letter proposes an end to end network named Multiple features integrated with convolutional long-short

Image matching is a quite time-consuming task for Structure-from-Motion (SfM). In this paper, a Bag-of-Words (BoW) model that reduces the feature dimensions and introduces image spatial locations is proposed to improve the efficiency and reliability of SfM. The whole workflow includes three steps. Firstly, principal component analysis (PCA) is used to reduce the high-dimensional features to low-dimensional

Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle

The mountain hazard triggered by earthquake usually starts near the ridgeline. Knowing the distribution of the ridgeline is conducive to making sound judgements on the possible hazards after an earthquake. However, in the complex scenes of PolSAR images containing buildings, the ridgeline recognition process will produce incorrect judgements due to the existence of buildings. Aiming at the ridgeline

Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect – a lightweight depth sensor – for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual

In the present study, a novel water index has been proposed for mapping of open surface water in multispectral images and named as open surface water index (OSWI). The proposed index has been applied on Landsat-8 data to delineate the water area and the results are compared with those of existing water indices. The index is also applied on Sentinel-2A data and corresponding water pixels are extracted

Hyperspectral image (HSI) classification is one of the core techniques in HSI processing. In order to solve the problem of scarcity of labelled samples, a novel HSI classification framework based on mixture generative adversarial networks (MGAN) is proposed in this letter. Firstly, to overcome the drawback that MGAN cannot be directly applied for classification, a category multi-classifier is introduced

Millimetre wave radar is an emerging technology that can monitor vital signs without contact. This unique feature is very suitable for some particular situations, such as burn patient monitoring. Currently, electrocardiogram (ECG) is still the most common approach for monitoring heart disease. Deep learning algorithms have already been applied to classifying ECG recordings and have achieved good diagnostic

Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional

To improve the quality of green tea, low light stress has been used to increase the chlorophyll-a (chl-a) content of tea leaves, although shading treatments sometimes lead to early mortality of tea trees. Therefore, in situ measurement of chl-a and chlorophyll-b (chl-b), which are markers for evaluating light stress and response to changing environmental conditions, can be used to improve tea tree

The Hyperspectral InfraRed Atmospheric Sounder (HIRAS), the first Chinese sun-synchronous hyperspectral infrared sounder onboard FengYun 3D (FY-3D), was launched on 15 November 2017. It will play a significant role in improving forecast accuracy of numerical weather prediction (NWP) systems. However, sun glint effects on the FY-3D HIRAS 3.9 μm channels cannot be currently estimated by radiative transfer

In this letter, we proposed a matting model guided variational pansharpening method with fractional order gradient transferring constraint to fuse a low-resolution (LR) multispectral (MS) and a high-resolution (HR) panchromatic (Pan) image to an HR-MS image. Specifically for the proposed model, we not only used the commonly local spectral fidelity constraint for spectral information preserving, but

The present study evaluate horizontal and vertical accuracy of seven open-source digital elevation models (DEMs) having moderate-to-high resolutions viz. 30 m Shuttle Radar Topography Mission (SRTM1), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), Advanced Land Observing Satellite World 3D (AW3D30), and Cartosat DEM (CartoDEM), 90 m TerraSAR-X add-on for Digital

Many existing techniques for estimating the optical properties of a body of water directly from a hyperspectral remote sensing spectrum are based on the idea of ‘spectral matching’ – that is, input parameters in a forward model are systematically varied until the measured and modelled spectra are sufficiently similar. This is usually done by using numerical optimization methods with a simplified forward

In this study, aerosol optical depth (AOD) and fine-mode fraction (FMF) with a 3 km resolution are retrieved from Geostationary Ocean Colour Imager (GOCI) data using a multi-temporal method. The retrieved results are input into a physical model for estimating the fine particulate matter (PM2.5) based on instantaneous satellite-based measurements. The other two input parameters of the model are planetary

ABSTRACT This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency

This paper discusses the estimation of rice yield from Water Cloud Models (WCM) with different layers by a single X-band (8–12 GHz) Synthetic Aperture Radar (SAR) image, from a satellite named TerraSAR. A simulated annealing (SA) algorithm, with a ‘leave-one-out’ cross-validation method, was applied. The results were related to actual rice parameters, with a coefficient of determination (R 2) of 0

As an important part of the ecosystem, wetlands provide varies of ecological functions while they have also been increasingly threatened and degraded. Therefore, it is necessary to protect wetlands with effective monitoring measures such as classification. Considering the quick development of multispectral sensors, multispectral images are available to classify wetlands. The current method, however

Tree detection and counting have been performed using conventional methods or high costly remote sensing data. In the past few years, deep learning techniques have gained significant progress in the remote sensing area. Namely, convolutional neural networks (CNNs) have been recognized as one of the most successful and widely used deep learning approaches and they have been used for object detection

In this paper, a novel modification applies to the Band-Dependent Spatial Detail (BDSD) framework to improve the fusion performance interestingly. Joint multiplication terms have been added to the conventional BDSD framework to make it nonlinear BDSD. It has been shown that joint product terms would lead to a better estimation of detail maps for each spectral band. To support the idea presented here

The large number of bands, the huge amount of information and the high band correlation in hyperspectral images bring great difficulties to the band selection of hyperspectral images. In the basic Particle Swarm Optimization (PSO), the learning factor and inertia factor are fixed, which limits the exploratory ability of the algorithm and cannot balance the global and local relations well. Therefore

Monitoring the dynamics of snow and glaciers in the Indian Himalaya has always attracted the attention of the remote sensing community. The snow water equivalent (SWE) represents the amount of water contained in the snowpack, and it is a product of snow depth (SD) and snow density ( ρ s ). The estimation of SD at high spatial and temporal resolution is still a challenge, especially in rugged mountainous

Convolutional Neural Networks (CNNs) are widely used in various fields, and have shown good performance in hyperspectral image (HSI) classification. Recently, utilizing deep networks to learn spatial-spectral features has become of great interest. However, excessively increasing the depth of network may result in overfitting. Moreover, in HSI classification, the existing network models ignore the strong

Soil-available nutrients (SANs)are essential for crop growth and yield formation. Appropriate variable rate fertilization (VRF) can control SAN at a normal level to avoid unnecessary damage to sustainable production capacity. The precondition of optimizing the application of VRF is obtaining the real-time status of SAN. A new method for SAN estimation has been proposed by integrating modified World

Multichannel synthetic aperture radar (SAR) is an effective method to achieve high-resolution wide-swath (HRWS) imaging simultaneously. Many algorithms have been proposed to reconstruct stationary targets. However, moving targets cannot be well reconstructed through traditional reconstruct algorithm for static scenes and the ghost targets of the moving targets will have a great influence on SAR image

The convolutional neural network (CNN) is widely used for image classification because of its powerful feature extraction capability. The key challenge of CNN in remote sensing (RS) scene classification is that the size of data set is small and images in each category vary greatly in position and angle, while the spatial information will be lost in the pooling layers of CNN. Consequently, how to extract

Accurate soil moisture (SM) data are key for climate and surface-atmosphere simulations associated with prediction and analysis of weather and climate variability. Here, we assessed in situ vs. remotely sensed SM discrepancies in a humid watershed during an extreme drought and an arid watershed under peak dry-season conditions. Using in situ SM measurements from the Soil and Climate Analysis Network

This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes

Aircraft detection in remote sensing imagery has drawn much attention in recent years, which plays an important role in various military and civil applications. While many advanced works have been developed with powerful learning algorithms in natural images, there still lacks an effective one to detect aircraft precisely in remote sensing images, especially in some complicated conditions. In this

This paper presents a multispectral image fusion method for remote sensing applications by using an improved estimation of detail maps. This estimation is achieved by considering a linear combination of the other MultiSpectral (MS) bands as well as their joint multiplications. A comparison with seven existing methods is carried out based on three public domain datasets and by considering commonly used

This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency properties of the described targets. The MTCS is used to jointly classify the original SAR image and its BVMD components. So, the merits of BVMD

(2020). Remote Sensing Letters contribution to the success of the Sustainable Development Goals - UN 2030 agenda. Remote Sensing Letters: Vol. 11, No. 8, pp. 715-719.

In this paper, a novel approach is proposed for Synthetic Aperture Radar (SAR) target classification based on multi-aspect multi-feature collaborative representation. Firstly, principal component analysis (PCA), wavelet and 2-dimensional slice Zernike moments (2DSZM) features are extracted from SAR images. Next, based on the strong correlation among the adjacent aspect SAR target images, we extend

An adaptive-trimming-depth-based constant false alarm rate (ATD-CFAR) ship detector of heterogeneous environment in SAR imagery is proposed in this letter. Traditional CFAR detectors generally use all samples in the background window for parameter estimation. However, in the heterogeneous regions, these detectors will overestimate the parameters used for statistical modelling due to the interference

In this letter, we proposed a novel deep feature manifold embedding method to improve feature extraction ability of traditional deep learning methods. This method first obtains deep features of hyperspectral image (HSI) from a trained autoencoder. Then, an intrinsic graph and a penalty graph are constructed to discover the discriminant manifold structure of deep features. Finally, the deep features

The instrument used for ocean colour remote-sensing works in the visible wavelengths, and the presence of clouds frequently lead to invalid observations. The formation of clouds is known to be influenced by mesoscale oceanic processes (e.g., eddies and temperature fronts), but these influences are often overlooked in missing data reconstructions. By analysing more than 10 years of chlorophyll-a (chl-a)

This paper describes an approach to forest growing stock volume (GSV) estimation based on remotely sensed optical data in red and near-infrared (NIR) bands collected during the period of persistent snow cover. The approach was applied to Sentinel-2 reflectance measurements over forest with snow-covered understory in the north-eastern part of Russian Kostroma region. An in-house dataset with a forest

Detecting the water surface area and its temporal changes is essential in water resource management. Multispectral satellite data are applied extensively to monitor the surface water dynamics because of their repeated coverage. This letter monitors waterbody dynamics in a high temporal resolution from Sentinel-2 and Landsat 8 images by using the Google Earth Engine (GEE) platform. Water index, automatic

For radar network, each radar can obtain the two-dimensional (2D) inverse synthetic aperture radar (ISAR) image from the corresponding observation angle independently. Taking advantage of the multi-view observation via radar network and the projection relationship in ISAR imaging, the three-dimensional (3D) image of the target can be reconstructed by the inverse-projection principle. However, it is

The angle of repose and volume of granular piles are important measures in many fields. This letter presents a new method for rapid estimation of the angle of repose and volume of grain piles using terrestrial laser scanning data acquired from a single site. Compared with some existing methods that require point cloud registration and surface reconstruction, the proposed method is simple and easy to

(2020). Editorial: A new start with motto 'festina lente' Remote Sensing Letters: Vol. 11, No. 7, pp. 609-610.

The study of sea surface temperature (SST) in coastal water is of great significance for navigation, aquaculture and military. Numerous studies have been conducted to predict this parameter in recent years. The fluctuation of SST is periodic, and it shows different changing patterns over different timescales. At present, most investigations on SST ignore the influence of multiscale features on the

Urban villages are a characteristic settlement type characterized by preserving their morphological characteristics embedded in sharp contrast in modern, high-rise developments found especially in fast growing urban agglomerations of China. They serve very important socioeconomic functions in terms of the provision of cheap housing for rural-urban migrants, but they are also considered controversial

Morphological building indexes (MBI) have proven to be effective tools for automated building spatial-feature-extraction tasks in images from urban areas. However, owing to the intrinsic shortcomings of MBI, commission and omission errors occur in regions with spectral properties similar to those of buildings and dark heterogeneous roofs, respectively. Some targets (such as bright bare land or roads)

After an attack the by pine wood nematode, pine tree needles turn red. Using convolutional neural networks (CNNs) based object detection methods, machines can detect red-attacked trees. However, most deep learning object detection algorithms (such as Faster R-CNN and YOLO among others) often require a large number of labelled training datasets, where in each image every object must be given a bounding

Disparity map quality assessment is crucial to evaluate the accuracies of stereo matching algorithms. Several widely used measures such as root mean square (RMS), mean absolute error (MAE) and bad matching pixels (BMP) have been proposed to evaluate the similarity between disparity map and ground truth (GT) map. These measures are based on grey-scale errors while ignoring the structural similarity

Unsupervised representation learning plays an important role in remote sensing image applications. Generative adversarial network (GAN) is the most popular unsupervised learning method in recent years. However, due to poor data augmentation, many GAN-based methods are often difficult to carry out. In this paper, we propose an improved unsupervised representation learning model called multi-layer feature

The United Arab Emirates (UAE) has seen an increase in oil extraction and oil refining processes. Toxic pollutants such as sulphur dioxide (SO2), black carbon (BC), carbon monoxide (CO) and organic carbon (OC) are released during oil refinery processes. These toxic pollutants have severe negative impacts on the climate, environment and human health. In this study we investigate the trends of BC, CO

In this paper, the zlog(z) based estimator for constant false alarm rate (CFAR) detection in Weibull clutter is proposed. This estimation method is obtained in terms of the digamma function where the estimates of the shape parameter are determined by the interpolation tool. The non-integer order moments estimator (NIOME) is also given and coincides the zlog(z) estimation results for low values of the

The sound speed profile (SSP) is a key dynamic factor affecting underwater acoustic propagation, and it is crucial to obtain SSP accurately in real time. A new scheme to improve the reconstruction performance of sound speed profile with multi-source observations using self-organizing map (SOM) method was proposed in this study. Given that the inverted echo sounder (IES) data and mix layer depth (MLD)

Learning discriminative and robust features is crucial in remote sensing image processing. Many of the currently used approaches are based on Convolutional Neural Networks (CNNs). However, such approaches may not effectively capture various different semantic objects of remote sensing images. To overcome this limitation, we propose a novel end-to-end deep multi-feature fusion network (DMFN). DMFN combines

Satellite-derived sea surface temperature (SST) presents discrepancies in both magnitude and temporal variability in the wind-driven coastal upwelling region off the Cape Frio (23°S) in Southeastern Brazil. Level 2 (L2) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, and Level 4 (L4) SST products are compared with in situ measurements. Strong positive biases up to

In this letter, we focus on the high-resolution imaging of ship targets under sparse aperture condition and its application in three-dimensional (3D) imaging. A bi-static high-resolution algorithm dealing with the sparse echo condition is proposed, which consists of the following processing steps: (1) A side-lobe apodization–multiple orthogonal least-square method is proposed to recover the complete

ABSTRACT There are a wide range of satellite sensors used for water quality measures with various spatial resolutions. Here, we focus on spatial resolution because some estuaries are smaller than the satellite nadir pixel width, and multiple pixels within an estuary are required for quality assurance . We used the United States Environmental Protection Agency’s (US EPA) Estuarine Data Mapper polygon

Internal waves represent an important mechanism of energy exchange influencing primary production through the turbulent transport of nutrients and changes in the intensity of light available for photosynthesis. Internal solitary waves (internal solitons) appear in packages and retain their shape and speed even after interacting with other internal waves. We detected solitons in the inner Patagonian

Since the conditional random field (CRF) model can integrate spectral and spatial-contextual information of high spatial resolution (HSR) remote sensing images in a unified framework, it becomes an effective approach to optimize the classification results. However, the results of traditional classification methods based on the CRF are sensitive to the parameters. In this paper, an adaptive conditional

In most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the

Modelling accurate ground surfaces in urban areas is important for surveying and mapping. Breaklines along roads are critical for both digital elevation models and high precision maps. This article presents a new breakline-preserving ground interpolation method for point clouds acquired by mobile laser scanning (MLS). The proposed method needs only point coordinates as input. It first initializes unknown