ABSTRACT A novel deep learning architecture called Attention-Residual block-Embedded Adversarial Networks (AREANs) is proposed in this letter, which can give the robust pixel-wise scene understanding in remote sensing images without any post-processing and additional data. The generator of AREANs, a novel designed encoder-decoder structure network, takes full advantage of Attention-Residual block to

ABSTRACT Using deep learning, the supervised hyperspectral image (HSI) classification methods are based on the ideal assumption of closed sets, in which all testing classes are defined a priori. However, it is impossible to collect all classes while training in the open set setting where unknown classes can be submitted while testing. Traditional deep neural networks lack the ability to perceive the

ABSTRACT Estimating surface water using satellite data can improve estimates and predictions for hydroelectric energy generation. The goal of this research is to identify the best threshold method and image band combination to detect seasonal surface water at the basin scale. This paper explores Otsu, Minimum Entropy, and average of Otsu-Minimum Entropy threshold algorithms using Sentinel-2 narrow

ABSTRACT Landsat and Sentinel-2 are two freely accessible satellite data that are relevant for global land cover monitoring. However, the uses of the latter data set are growing because of its higher spatial resolutions and the availability of benchmark data sets for deep learning applications. In this study, we integrate a style transfer (perceptual loss estimation from Sentinel 2 benchmark data)

ABSTRACT Applying machine learning to hyperspectral remote sensing is a new way to solve practical problems. A new method that combines spectral indexes and Naive Bayes, named SINB, was proposed to distinguish heavy metal pollution in crops. First, the CLPIOR, CLPICR, and CLPIFOD (Copper-Lead Pollution Index of the original spectrum, continuum removed spectrum, and first-order differential spectrum)

ABSTRACT Remote sensing scene classification (RSSC) is one of the fundamental and challenging tasks for remote-sensing understanding and interpretation. How to construct discriminative features is crucial for scene classification. It is generally acknowledged that integrating convolutional features from different layers can significantly improve scene classification performance. However, some existing

ABSTRACT Optimum interpolation (OI) analysis is performed on the sea surface temperature (SST) products retrieved from the Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR). The oriented elliptic correlation scale is employed on the SST data and then compared with those from the traditional rectangular correlation scale in the OI analysis. The results were also evaluated against the daily SST

ABSTRACT Due to the casualties and economic losses that can be caused by building collapse after an earthquake, building damage assessment is a very important part of earthquake disaster evaluation. As a result of the advantages of polarimetric synthetic aperture radar (PolSAR) imaging, both the accuracy and speed of the damage assessment can be guaranteed in the process of building damage identification

ABSTRACT The atomic norm minimization (ANM) based gridless recovery approaches can completely obviate the grid mismatch problem of discrete compressed sensing methods by working directly on a continuous dictionary, which have attracted considerable interest in sparse inverse synthetic aperture radar (ISAR) imaging. In order to exploit the joint sparsity in the multiple measurement vectors (MMV), a

ABSTRACT This letter combines the multi-mode representations extracted by bidimensional empirical mode decomposition (BEMD) and deep residual networks (ResNet) for synthetic aperture radar (SAR) target recognition. Bidimensional intrinsic mode functions (BIMFs) are generated by BEMD to describe the target characteristics in SAR images. A special ResNet is designed and trained for each layer of BIMFs

ABSTRACT The positioning accuracy of Ziyuan-3 01 (ZY-3 01) and Gaofen-7 (GF-7) panchromatic imagery, obtained during the early deployment of the satellites, was verified. Particularly, this paper first validated the geometric positioning accuracy of GF-7 panchromatic imagery. Based on the rigorous geometric model of surveying and mapping satellites, this study applied a geometric calibration (GC) model

ABSTRACT Meta-learning is an effective way to deal with the few-shot problem that only very few annotated data samples are available for training the model. In remote sensing scene classification, previous methods with meta-learning often apply a shallow neural network due to lacking sufficient annotated training data that can support the larger number of parameters of a deep neural network (DNN).

ABSTRACT There is increasing interest in the use of unmanned aerial vehicles (UAVs) as cost-effective, high-resolution alternatives to satellites for the environmental monitoring of large areas. In this study, we tested the feasibility of using UAV-acquired soil surface temperature to monitor groundwater level in a peatland undergoing groundwater restoration to mitigate fire risk (Tanjung Leban, Indonesia)

ABSTRACT Recently, convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) image classification because of their powerful feature extraction ability and high performance. However, extracting discriminative features with limited training samples is still a challenge. Moreover, some samples may be image edge samples, which often contain multiple image categories

ABSTRACT This work focused on the rain-induced characteristics of synthetic aperture radar (SAR) at the C- and X-bands. Six SAR images were collected in tropical cyclones: four C-band dual-polarized Sentinel-1 (S-1) images during the 2016 hurricane season and two vertical–vertical) polarized TerraSAR-X (TS-X) images of Hurricane Sandy (2012). Wind speeds were retrieved from the S-1 images at the vertical–horizontal

ABSTRACT Recently, deep learning models have achieved remarkable results in hyperspectral imagery (HSI) classification. However, most deep learning models tend to struggle due to the serious overfitting problem under the condition of small sample. For this purpose, we design an end-to-end classification framework and optimize it with the idea of meta-learning, to improve the accuracy and robustness

ABSTRACT Monitoring the melting of Greenland ice using various sensors is of great importance due to global sea level rise. The mass changes in Greenland can be observed with the GRACE (Gravity Recovery and Climate Experiment) mission from 2002 to 2016. The GRACE limitations and noise are due to the geometrical and instrumental properties along its orbit, which requires investigations for further improvement

ABSTRACT Monitoring large-scale flood damage can be complicated and costly. Damages caused by floods affect also the agricultural sector. Permanence, height and quantity of stagnant water can significantly influence crop yield. Many studies exploit satellite data to map flooded areas, but only a few are focused on the timing of water persistence. This work refers to the river Sesia flooding event which

ABSTRACT Airglow emissions which originate from the mesospheric and thermospheric altitudes have been routinely being monitored at Kolhapur (16.8° N, 74.2° E), Maharashtra, India, using ground base remote sensing imagers. We note that the observable amplitudes of very small-scale waves during April 2020 were significantly smaller than the regular observations. We investigate the reason for these low

ABSTRACT Various minerals present distinct spectra due to different constituent ions or groups. However, variable imaging conditions of hyperspectral imagery greatly affect the mineral spectra, which makes it difficult to identify minerals. Attenuation spectral absorption index (ASAI) is suggested to extract absorption features from mineral diagnostic bands, which combines spectral absorption index

ABSTRACT Sea surface temperature (SST) prediction plays an important role in planning marine operations and forecasting climate. With the rapid development of remote sensing technology, there are plenty of SST data available for scientific research. However, most previous studies ignored the quality of SST dataset and the importance of data at each time step, which limited the performance of prediction

ABSTRACT Carbon use efficiency (CUE) of vegetation in Western Sichuan Plateau (WSP) can influence ecological safety and economic development in the Sichuan Basin and Yangtze River Basin in addition to its importance in determining local ecological environment and development of animal husbandry. Temporal-spatial variations of vegetation CUE as well as its responses to climate changes were investigated

ABSTRACT Recent research on hyperspectral image (HSI) classification has primarily focused on deep learning methods. Although these methods can automatically mine HSI classification features, they typically require many labelled samples to ensure sufficient classification performance. When there are fewer labelled training samples, the manual feature extraction design rules are critical for HSI classification

ABSTRACT With increase in urbanization and Earth Sciences research into urban areas, the need to quickly and accurately segment urban rooftop maps has never been greater. Current machine learning techniques struggle to produce high accuracy maps in dense urban zones where there is high image noise and foot print overlap. In this paper, we evaluate a training methodology for pixel-wise segmentation

ABSTRACT This paper introduces a new object-based spectral-spatial classification method for hyperspectral image. The kernel principal component analysis (KPCA) is firstly performed over subspaces (KPCAsub) derived from the original spectral domain, which incorporates linear information with nonlinear formulation. The obtained image is then processed via a feature-level fusion with superpixel segmentation

ABSTRACT Weighted nuclear norm minimization (WNNM) has produced remarkable denoising results; however, it still has some limitations, including only being able to measure the similarity of noisy patches by Euclidean distance, fixing the feedback proportion of method noise for all noise levels, and establishing an inflexible number of iterations for each image. In this paper, three strategies are used

ABSTRACT The climate change is severely impacting global agriculture, especially that of the developing world where non-availability/awareness of advanced technologies at the root level is a significant limitation. Adopting advanced and sustainable technologies, such as space technologies can be a successful approach in minimizing the severe impacts as evident from the developed first-world nations

ABSTRACT A lockdown due to the novel coronavirus (COVID-19) resulting in the closure of largest industries in the world for a period of 2 months was enough to cause a drop of 7% of the anthropogonic carbon dioxide (CO2) in the atmosphere. Besides, the atmosphere oceans play a key role to buffer the greenhouse effect. Herein, the resultant reduction in CO2 emissions on the productivity of the global

ABSTRACT In this paper, a different approach based on convolutional neural networks (CNNs) is proposed to generate digital surface model (DSM) from a single high-resolution satellite image. In this regard, an approach based on a deep convolutional neural network was designed. The proposed CNN has an encoder-decoder structure to extract multi-scale features in the encoding part and estimate the height

ABSTRACT Oil spills cause great environmental damage and the ability to identify them accurately is important to prompt oil spill response. A power fitting method of radar echo is proposed in this paper to detect oil spills on sampled X-band marine radar images. In this method, first, the marine radar image is pre-processed, including coordinate transform and noise reduction. Then, the difference between

ABSTRACT The death of Sir John Houghton a few months ago revived memories of pioneering figures who have left an indelible signature on the science of remote sensing. This article summarizes the main contributions of this scientist’s work, which include, among others, the foundation of remote sensing - especially in early satellite technology- the selective chopper radiometer, the atmospheric sounding

ABSTRACT Accurate roof segmentation is one of the key steps for automatically constructing three-dimensional (3-D) building models. Building roofs can differ significantly in terms of their size, shape complexity, and number, rendering many existing airborne Light Detection And Ranging (LiDAR) roof segmentation methods ineffective. Thus, the applicability and precision of these methods need to be improved

ABSTRACT Automatic change detection is an important and difficult task in the field of remote sensing. In this study, a deep Siamese convolutional network based on the fusion of high- and low-level features is proposed for change detection in remote sensing images. Given that low-level features correspond to low-order ones (e.g., texture) that are sensitive to change and that high-level features can

ABSTRACT Anchor-based methods, which require a large number of pre-set anchors, have been widely used for oriented object detection in remote sensing images. However, the definitions of anchors’ sizes, aspect ratios and quantities are heuristic and the use of anchors is time-consuming. In this paper, we propose an oriented one-stage anchor-free detector for aerial image object detection. Arbitrary

ABSTRACT Majuli is the world’s largest Riverine Island, formed by the headward erosion and migration of the Brahmaputra River. It is a part of the floodplains, which causes loss of arable lands, crops and farmers’ livelihood. To cope and adapt, farming communality adopted hydroponics farming. In this study, high-resolution satellite data (~3 m) were deployed to detect hotspots of hydroponics growing

ABSTRACT In the spaceborne inverse synthetic aperture radar (ISAR) imagery for space targets, the noise environment can be worse than traditional imaging scenarios because of the signal decay caused by the long radial range. Parametric translational motion compensation (TMC) method based on the image quality optimization has been proved to be useful in the low signal-to-noise ratio environments. However

ABSTRACT Aiming at the low detection rate and high false alarm in small ship detection in SAR images, we propose a small-scale ship detection algorithm based on convolutional neural network in this paper. First, we redesign the feature extraction network according to the characters of ship targets in SAR images. The modified network can enrich the spatial and semantics information of small ships. Then

ABSTRACT This letter proposes to use two k-nearest neighbours (k-NN) based algorithms namely: randomized k-NN and weighted k-NN for feature selection with two hyperspectral datasets. Two embedded feature selection methods, extreme gradient boosting and random forest classifier and one wrapper-based approach using support vector machine classifier, were used for comparison in terms of classification

ABSTRACT The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing; however, the

ABSTRACT This work investigates the effect of the resampling spatial resolution (RSR) on the retrieval of ocean surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images. For this purpose, 358 RADARSAT-2(RS-2) fine-beam quad-polarized SAR images and in-situ National Data Buoy Centre (NDBC) and China State Oceanic Administration (SOA) buoy measurements from selected geographic locations

ABSTRACT Zero Doppler centroid imaging is of great significance in synthetic aperture radar interferometry (InSAR). Because the moon’s orbit is elliptical and its orientation cannot be changed artificially, the zero Doppler centroid of lunar-based synthetic aperture radar (LB-SAR) is not steerable and will vary with lunar rotation and SAR system’s position on lunar surface. Therefore, the distribution

ABSTRACT A pixel-based spectral matching method using enhanced vegetation index (EVI) time series from GaoFen-1 (GF-1) Satellite was developed based on spectral similarity value (SSV) and iterative thresholding to map irrigated areas for varying and scattered croplands at high resolution in order to improve understanding of dynamic irrigation pattern. The approach was tested in a typical irrigation

ABSTRACT Speckle reduction is a fundamental problem in coherent imaging systems. In this paper, to suppress the speckle in SAR images, we propose a novel truncated nonconvex nonsmooth model. It incorporates a truncated nonconvex regularization term and an I-divergence fidelity term. The truncated ℓ p n o r m ( 0 < p < 1 ) regularization can better recover neat edges and simultaneously prevent contrast

ABSTRACT A method of correcting the slant-range error of the space-borne interferometric synthetic aperture radar (InSAR) system aboard the TH-2 satellites is proposed. First, we calculate the tropospheric-delay using the third-order fractional mapping-function method, and we calculate the ionospheric-delay using the ray-tracing method. The calculations show that the atmospheric-delays for the TH-2

ABSTRACT Staggered synthetic aperture radar (SAR) is an advanced concept for high-resolution wide-swath (HRWS) imaging, which overcomes the problem of blind ranges by continuously varying the pulse repetition frequency (PRF). For better performance of information recovery in blind ranges, data are typically highly oversampled in azimuth for staggered SAR system. Thus a huge data volume onboard is required

ABSTRACT Wide field imager (WFV) cameras are Chinese high-resolution optical sensors in the GF-1 satellite, and the data from WFV cameras have been applied to environmental monitoring. In this paper, for wide swath observation of WFV, the blue/red correlation is more suitable for retrieving Aerosol Optical Depth (AOD). the blue/red correlation of surface reflectance was analysed by Moderate Resolution

ABSTRACT Soils trafficability is measure of soil strength required to support the manoeuvring of military vehicles. Trafficability can be measured with the tests performed using cone penetrometer and moisture metre to understand the complexity of phenomenon. Empirical relation can be developed through the outcomes of these tests. This empirical relation can be used in tandem with geospatial data to

ABSTRACT Hyperspectral images (HSIs) are usually composed of hundreds of bands, which are highly correlated and redundant, leading to dimension disaster and high complexity of classification. In this paper, we propose an end-to-end dense spatial–spectral attention network (DSSAN) for hyperspectral image band selection to reduce the complexity of classification while ensuring the accuracy. In this network

ABSTRACT This research aims to provide a brief evaluation of remotely sensed estimates in representing gross primary production (GPP) over pasture in the Northeast Region of Brazil. We used GPP data measured in situ during a micrometeorological experiment performed in a pasture area located in the NEB region using the eddy covariance technique. Remotely sensed GPP was obtained through the MOD17A2H

ABSTRACT This paper proposes convolutional neural network jointed with long short-time memory (CNN_LSTM) and Seq2Seq based on convolutional operation (Convolutional Seq2Seq), which the fully connected operation of Seq2Seq is replaced by convolution, and the attention mechanism of Seq2Seq is improved to monitor changes in water bodies. Convolutional Seq2Seq and CNN_LSTM can extract the temporal and

ABSTRACT In this study, we introduce a non-local block of the attention mechanism into capsule neural network (CapsNet) to form a non-local capsule network (NLCapsNet) for hyperspectral remote sensing image (HSI) classification. The presented NLCapsNet uses global information from input images and has a powerful representation of the capacity and spatial relationships among HSI features. It can effectively

ABSTRACT The present study reports a univariate predictive model for Total Ozone (TO) concentration derived from Ozone Monitoring Instrument (OMI)/Aura observations. Using the Markovian approach through proper discretization method it has been observed that the second-order Markov Chain can represent the time series of TO Concentration. Considering daily data spanning from 2015 to 2019, consisting

ABSTRACT The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational

ABSTRACT Multichannel synthetic aperture radar (SAR) systems in azimuth can realize high-resolution and wide-swath (HRWS) imaging, which has attracted intensive research interests. The azimuth ambiguities caused by non-uniform sampling can be suppressed by digital beamforming technique. However, the presence of inevitable channel errors in the multichannel SAR system deteriorates the performance of

ABSTRACT Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging task. This study utilises machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training

ABSTRACT The lockdowns imposed worldwide to curb the coronavirus diseases (COVID-19) spread has positive effects on the environment. However, it is unclear what fraction is caused by weather and what is related to lockdown. Here we used Global Navigation Satellite System (GNSS) height anomaly time series to quantify the spatio-temporal characteristics of lockdown-induced noise anomalies at 231 selected

ABSTRACT ABSTRACT: The two-scale Model (TSM) is broadly employed for studying EM scattering from rough sea surface due to its advantage of simple and practical. However, it suffers from a drawback in the prediction of microwave scattering from sea surface. That is the TSM cannot provide accurate predictions for hh polarized scattering coefficients. To overcome this problem, a modified TSM (MTSM) is

ABSTRACT This study evaluates various combinations of data fusion techniques at Pixel, Feature, and Decision level for crop classification using Sentinel-1 Synthetic Aperture Radar (SAR) data and Resourcesat-2 LISS (Linear Imaging Self Scanning) III, optical data for Yadgir District of Karnataka, India. For Pixel level data fusion, techniques such as brovey transformation (BT), principal component

ABSTRACT If crops were polluted by heavy metals, the spectra will change. Therefore, the variation information of spectral changes has become an important basis for heavy metal pollution monitoring. Based on spectral frequency domain, we studied the spectra of maize leaves. Combined with time-frequency analysis method, we proposed DDCR-Db (Second-order Differential Continuum Removal- Daubechies) method

ABSTRACT In the present communication, using cloud-type distribution derived from CloudSat observations during the years 2006–2010 and vertical structure of LH derived from Tropical Rain Measuring Mission (TRMM) observations during the same period over the Indian summer monsoon (ISM) region, an attempt is made to relate the both. First, the regions where particular type of clouds forms predominantly