Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Network (CNN) for Crop - Classification of Drone Acquired Hyperspectral Imagery

Hyperspectral remote sensing has gained prominence in the past two decades. The high resolution imagery of Unmanned Aerial Vehicle (UAV), Terrestrial Hyperspectral spectroradiometer (THS) have gained popularity. In this study, we develop a Convolutional Neural Network (CNN) based architecture to accurately classify UAV, THS dataset for the region of Bangalore, India. To carry out feature extraction, classification, prediction and analysis a CNN Conv-4 and CNN Conv-6 model is designed with varying kernel sizes thus, extracting multiple features. The results show a visual and quantitative measure of how CNN Conv-6 has better capability and higher accuracy. Also, a graphical plot of training, validation accuracy and loss as a function of number of iterations is generated as an output at the end of prediction. This is to clarify the threshold limit for number of epochs required to run the model for classification and prediction.
A computational approach to constructive a priori error estimate for finite element approximations of bi-harmonic problems in nonconvex polygonal domains

In the numerical verification method of solutions for nonlinear fourth order elliptic equations in nonconvex polygonal domains, it is important to find a constant in the constructive a priori error estimate for the finite element approximation of bi-harmonic problems. We show these procedures by verified computational techniques using the Hermite spline functions for the two dimensional Lshaped domain. Several numerical examples which confirm the actual effectiveness of the method are presented.
Large Remote Sensing Image Super Resolution Using Self Attention Conditional Coordinate Network

Optical imagery is one of the most important sources for model remote sensing. Super resolution of the optical data could be very useful for industry and academia. Conventional solutions from computer vision only offers super resolution on small images describing relatively smaller scene and optimized towards human perception. In this paper, we proposed a new deep learning model for large remote sensing image super resolution. By using conditional coordinate and self-attention, the model could achieve arbitrary large image super resolution task with special focus on correctness of the details. Numerical evaluation of the model is carried out based on real satellite remote sensing data. Result shows a promising performance with PSNR $> 33\ \text{dB}$ and SSIM $> 0.93$.
Estimation of Coniferous Forest Height using Stereo Images of GF-7 Satellite and Airborne Lidar Data

Forest height is a key parameter of forest spatial structure, which is useful for forest management and ecosystem modelling. High resolution stereo images of satellite can be used to reconstruct the surface of ground objects. The objective of this study is to estimate the coniferous forest height combining stereo images of Gaofen-7 (GF-7) satellite and digital terrain model (DTM) extracted from airborne light detection and ranging (LiDAR). The study site is characterized by boreal coniferous forest located in the northern China. The dense point cloud from stereo images was normalized by subtracting the LiDAR DTM. The forest height was estimated by linear regression between the extracted features of normalized point cloud and the field measurements of stand height. The results shown that the values of estimated height were high correlated with the height percentile of 50% (R2 = 0.79, RMSE = 2.34m). The stereo images of GF-7 satellite have the great potential for regional forest monitoring.
Analysis of Deep Learning 3-D Imaging Methods Based on UAV SAR

As an important development of traditional SAR 2-D imaging, Synthetic aperture radar (SAR) 3- D imaging's core is sparse signal processing. However, due to the nonlinear characteristics of sparse signal processing, it often needs iterative calculation, which makes it inefficient. Researchers have put forward some ideas of using deep learning neural networks to quickly solve nonlinear signal processing problems, but it is lack of comparative analysis of different network performances. Therefore, this paper analyzes the abilities of two deep learning neural networks (ISTA-Net and ADMM-Net) to solve the 3-D imaging problem of tomographic SAR. Their quantitative performance in imaging accuracy and imaging efficiency is emphatically discussed, which can provide theoretical reference for subsequent deep learning SAR 3-D imaging research. The effectiveness of the analysis is verified by the measured data of UAV SAR.
Effect of postdeposition anneals on the Fermi level response of HfO2/In0.53Ga0.47As gate stacks

The electrical characteristics, in particular interface trap densities, oxide capacitance, and Fermi level movement, of metal oxide semiconductor capacitors with HfO2 gate dielectrics and In0.53Ga0. 47As channels are investigated as a function of postdeposition annealing atmosphere. It is shown, using both conductance and Terman methods, that the Fermi level of nitrogen annealed stacks is effectively pinned at midgap. In contrast, samples annealed in forming gas show a large band bending in response to an applied gate voltage and a reduced midgap interface trap density compared to those annealed in nitrogen.
Evaluation of four Spatiotemporal Gap-Filling Methods in Crop Phenology Monitoring

The high spatial resolution land surface phenology (LSP) monitoring is often limited by the temporal discontinuity of high spatial resolution observations. Many gap-filling methods have been proposed for LSP monitoring, however, a thorough intercomparison and evaluation is still lacking. Four widely-used methods, including the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), Multi-year based model, and Spatiotemporal Shape Matching Model (SSMM) were selected to extract green-up date (GUD) based on the Harmonized Landsat and Sentinel-2 (HLS) dataset. The results of the four methods show consistency with those of PhenoCam sites (R2>0.64) and there is a lag phenomenon. Compared within 3×3 VIIRS pixel window, the difference between mean VIIRS GUDs and aggregated 30m GUDs is small and the mean absolute difference is less than 7 days. Comprehensively, SSMM has high consistency (R2=0.75) and smaller bias (Bias=7.3days), which shows more potential in LSP monitoring.
Asymmetric Training of Generative Adversarial Network for High Fidelity SAR Image Generation

In practical application, the research of synthetic aperture radar (SAR) target recognition has fallen into a bottle-neck due to the lack of samples. Data argumentation methods based on generative adversarial networks (GAN) have received widespread attention in solving this type of few-shot sample problem. However, the generated images suffer from various shortcomings, such as lack of diversity, low signal-to-noise ratio, blur, etc. In this article, the VAE-WGANGP is proposed, which combines GAN and variational autoencoder (VAE) to alleviate these shortcomings. The innovations of this paper are as follows: firstly, the generator of GAN is replaced with VAE, which constructs an asymmetric network ensuring the stability of GAN training; secondly, the asymmetric loss function is composed of four parts, including reconstruction loss, divergence loss, adversarial loss, and gradient penalty. In this way, the problem of gradient explosion or gradient disappearance is alleviated. The experimental results with the MSTAR dataset show that the images generated by the proposed model outperform the advanced technology with many similar deep features and achieve significant improvement in the target recognition accuracy rate.
Near-infrared studies during maximum and early decline of Nova Cephei 2014 and Nova Scorpii 2015

We present multi-epoch near-infrared photo-spectroscopic observations of Nova Cephei 2014 and Nova Scorpii 2015, discovered in outburst on 2014 March 8.79 UT and 2015 February 11.84 UT respectively. Nova Cep 2014 shows the conventional NIR characteristics of a Fe II class nova characterized by strong CI, HI and O I lines, whereas Nova Sco 2015 is shown to belong to the He/N class with strong He I, HI and OI emission lines. The highlight of the results consists in demonstrating that Nova Sco 2015 is a symbiotic system containing a giant secondary. Leaving aside the T CrB class of recurrent novae, all of which have giant donors, Nova Sco 2015 is shown to be only the third classical nova to be found with a giant secondary. The evidence for the symbiotic nature is three-fold; first is the presence of a strong decelerative shock accompanying the passage of the nova's ejecta through the giant's wind, second is the H$\alpha$ excess seen from the system and third is the spectral energy distribution of the secondary in quiescence typical of a cool late type giant. The evolution of the strength and shape of the emission line profiles shows that the ejecta velocity follows a power law decay with time ($t^{-1.13 \pm 0.17}$). A Case B recombination analysis of the H I Brackett lines shows that these lines are affected by optical depth effects for both the novae. Using this analysis we make estimates for both the novae of the emission measure $n_e^2L$, the electron density $n_e$ and the mass of the ejecta.
Crop Phenology Stage Forecasting and Detection Using NDVI Time-Series and LSTM

Forecasting crop phenology helps in crop production estimation, irrigation scheduling and crop identification. In this article’ we propose a methodology that uses the Normalized Difference Vegetation Index (NDVI), derived from the bands of an optical multispectral satellite (Sentinel-2) data for forecasting crop phenology stages using a Long Short-Term Memory (LSTM) network. Firstly, the LSTM network is built for forecasting the NDVI values of the potato crop, until the end of the season. Then the crop phenology estimation technique is applied to this forecast data to obtain the phenology points. The potato plots considered for the study were grown around the Gujarat state in India. The performance of the LSTM network on the test dataset is given by MAE≈ 0.19, MSE≈ 0.06 and RMSE≈ 0.23, and the phenology points are detected with a mean deviation of ±10 days.
Supergiant temperatures and linear radii from near‐infrared interferometry

We present angular diameters for 42 Luminosity Class (LC) I stars and 32 LC II stars that have been interferometrically determined with the Palomar Testbed Interferometer. Derived values of radius and effective temperature are established for these objects, and an empirical calibration of these parameters for supergiants will be presented as a function of spectral type and colours. For the effective temperature versus (V - K) 0 colour, we find an empirical calibration with a median deviation of AT = 70K in the range of 0.7 < (V - K) 0 < 5.1 for LC I stars; for LC II, the median deviation is AT = 120K from 0.4 < (V - K) 0 < 4.3. Effective temperature as a function of spectral type is also calibrated from these data, but shows significantly more scatter than the T EFF versus (V - K) 0 relationship. No deviation of T EFF versus spectral type is seen for these high-luminosity objects relative to LC II giants. Directly determined diameters range up to 400R ⊙ , though are limited by poor distance determinations, which dominate the error estimates. These temperature and radii measures reflect a direct calibration of these parameters for supergiants from empirical means.
Wind power forecasting using LSTM incorporating fourier transformation based denoising technique

Forecasting of wind power is necessary to remove the system operational uncertainties via ensuring more reliable inputs for power system scheduling and control. In order to achieve accurate forecast of wind power for up to 50 seconds (very short-term forecasting), this paper proposes the method Fourier Denoising combined with Long Short-Term Memory (FD-LSTM). The FD-LSTM cascades the output of the Fast Fourier Transform (FFT)-based denoising algorithm to the input of the Long Short-Term Memory (LSTM) forecaster. In this method, first, the inclusion of the FFT-based denoising algorithm ensures better and balanced performance for different forecasting horizons by removing the frequencies. Next, short-term forecasting by employing LSTM reduces the uncertainty and improves both the quality of the operation and the planning. In this paper, we first evaluate the performance of the proposed FD-LSTM method on increased forecasting horizons based on Mean Square Error (MSE), Mean Absolute Error (MAPE), and R-Squared, and compare the results against linear regression. Afterwards, the method is tested with data sets where false data is present. The results show that FDLSTM outperforms the other forecasting methods under the presence of false data.
Complex-Valued Sparse Long Short-Term Memory Unit with Application to Super-Resolving SAR Tomography

To achieve super-resolution synthetic aperture radar (SAR) tomography (TomoSAR), compressive sensing (CS)-based algorithms are usually employed, which are, however, computationally expensive, and thus is not often applied in large-scale processing. Recently, deep unfolding techniques have provided a good combination of physical model-based algorithms and the ability of neural networks to learn from data. In this vein, iterative CS-based algorithms can usually be un-rolled as neural networks with only 10 to 20 layers. When trained, it shows great computational efficiency for further TomoSAR processing. However, the learning architecture of neural networks built in this approach tends to result in error propagation and information loss, thus degrading the performance. In this paper, we propose to employ complex-valued sparse long short-term memory (CV-SLSTM) units to tackle this problem by incorporating historically updating information into the optimization procedure and preserving full information. Simulations are carried out to validate the performance of the proposed algorithm.
Key Region Extraction Via Scene Classification Model

Key regions are the similar regions of the scenes of the same category and they are the crucial and explicable areas. In this paper, we extract the key regions from the satellite images. The proposed method transfers the model of scene classification and obtains the result without the segmentation labels. First, the features of each layer of current scene classification models are extracted to find the relationships of key regions and scene classification labels. Second, we reconstruct the images from the relative features to obtain end-to-end results. There are areas of similar features between the images of the same categories and no areas of different categories. Last, we design the masks to obtain the key regions from the end-to-end results. The experiments were conducted on a typical scene classification dataset. The experimental result demonstrates the feasibility of extracting key regions by scene classification without relabeling the dataset.
Multi-Function Radar Work Mode Recognition Based on Encoder-Decoder Model

The multi-function radar (MFR) is capable of transmitting complex signals and achieving beam agility according to different tasks, thus leading to many challenges in the work mode recognition field. This paper, therefore, develops an Encoder-Decoder model based on the gated recurrent units (GRU) network to achieve work mode recognition. It involves the use of the Encoder structure to extract the temporal features and the work mode transition regulations of the intercepted pulse group sequence while leveraging the Decoder structure to decode the features and transition regulations as a part of the input for the next decoding process. Additionally, the label substitution (LS) method is utilized for improving the ability of the Decoder structure to recognize work mode under non-ideal situations. Simulation results show that the proposed method is less sensitive in the presence of non-ideal situations and shares better performance than existing work mode recognition methods.
Active Layer Thickness Throughout Northern Alaska by Upscaling from P-Band Polarimetric Sar Retrievals

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5–7 cm relative to validation samples, and RMSEs of approximately 10–12 cm relative to in situ ALT measurements.
Transfer Learning with Shared and Specific Structures for SAR Target Recognition

While most existing transfer learning methods map the source domain and the target domain data into a common space by sharing the model, the specific knowledge with private properties may be lost. To address this problem, we propose a transfer learning model with shared and specific structures for synthetic aperture radar (SAR) target recognition in this paper. Firstly, we design a convolutional neural network model with the shared subnetwork and the specific subnetworks. The shared subnetwork transforms the common information of the source and the target domain data into a common subspace, while the specific subnetworks transform the private information into the specific subspaces separately. The proposed model does not only extract the similar structure of the different domains but also reserve their private properties. Secondly, by considering the different contributions of the common-subspace features and the specific-subspace features for the final classification, we design adaptive feature weights to compute the domain features. Lastly, in addition to the classification loss, the model is trained by reducing distribution discrepancy loss, which establishes a knowledge transfer bridge from the labeled source domain to the unlabeled target domain. The experimental results verify the effectiveness of the proposed method.
COMPRESSIVE COHERENT STRUCTURES AT ION SCALES IN THE SLOW SOLAR WIND

We present a study of magnetic field fluctuations in a slow solar wind stream, close to ion scales, where an increase of the level of magnetic compressibility is observed. Here, the nature of these compressive fluctuations is found to be characterized by coherent structures. Although previous studies have shown that current sheets can be considered the principal cause of intermittency at ion scales, here we show for the first time that, in the case of the slow solar wind, a large variety of coherent structures contributes to intermittency at proton scales, and current sheets are not the most common. Specifically, we find compressive ( ), linearly polarized structures in the form of magnetic holes, solitons, and shock waves. Examples of Alfvénic structures ( ) are identified as current sheets and vortex-like structures. Some of these vortices have , as in the case of Alfvén vortices, but the majority of them are characterized by . Thanks to multi-point measurements by the Cluster spacecraft, for about 100 structures we could determine the normal, the propagation velocity, and the spatial scale along this normal. Independently of the nature of the structures, the normal is always perpendicular to the local magnetic field, meaning that k⊥ ≫ k∥. The spatial scales of the studied structures are found to be between two and eight times the proton gyroradius. Most of them are simply convected by the wind, but 25% propagate in the plasma frame. Possible interpretations of the observed structures and the connection with plasma heating are discussed.
Mae-Net: A Micro Network Architecture Evolutionary Search Method for Remote Sensing Image Scene Classification

Deep learning based remote sensing scene classification methods have become a research hotspot, but they can not fully mine the image information due to the architecture comes directly from natural image. The automatic search method-based network architecture has then attracted a lot of attention benefits by its ability to independently learn the network structure suitable for remote sensing data. However, in the process of search and sorting, slightly larger models with better performance after full training are often eliminated due to insufficient training. Moreover, the methods often spend a lot of time searching. In this paper, a micro network architecture evolutionary search method is proposed (MAE-Net), the contributions are reflected in the slightly larger model retention mechanism by two-layer multi-objective functions and the super network mechanism used to reduce search time through weight sharing. The effectiveness is proved by comparison with human expert and search based networks on NWPU45 dataset.
A Study on Automated Detection of Surface and Sub-Surface Coal Seam Fires Using Isolation Forest from Landsat 8 OLI/TIRS Images

Coal seam fire (CSF) affects various social, economical, and environmental aspects. Surface and sub-surface CSF are detected in the past by studying the anomalies of land surface temperature patterns. Most of them use manual observations and thresholds to separate CSF. Such techniques are also susceptible to other regions, which have high temperature and high mineral content. The proposed work presents a novel technique to detect surface and sub-surface CSF by treating them as outliers. It provides a multi-level anomaly detection technique using isolation forest, which is robust to the presence of other high temperature and high mineral content regions over the seasons.
A Discrimination Method of Water and Shadow Areas Based on Polarization Entropy of Sentinel-1 Data

Rapid and accurate extraction of water body information is fundamental to disaster assessment. However, in SAR images, shadows often appear similar to the water regions, so the commonly threshold-based water detection methods easily confuse them. The purpose of this paper is to use Sentinel-1 polarimetric SAR data to find a simpler and more effective method to identify shadow areas out of water bodies. Considering a strong dependence on intensity of Wishart classifier, the weak backscattered regions on the SAR images will be categorized into the same class. We firstly use $H/\overline{\alpha}$ Wishart to roughly classify the PolSAR data to obtain a relatively complete experimental study area. Then, we further discriminate the shadow regions from the study area based on the difference of the entropy between the water body regions and the surrounding environment. The experimental results based on Sentinel-1 measured data prove the effectiveness of this method.