Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense Cross-Scale Transformer with Channel Learning for Remote Sensing Scene Classification

The recent explosive on Transformer has suggested its potential to become the mainstream model for feature representation and classification of remote sensing. Although Transformer has excellent global modeling capabilities, it lacks inductive bias. In contrast, CNNs show excellent performance in computer vision due to their strong inductive bias. To solve the above issue, and benefit from combining the Transformer model with CNNs, a dense cross-scale Transformer is proposed for remote sensing scene classification. Firstly, the attention aggregation based feature pyramid network (A2 -FPN) is adopted to obtain the multi-scale features. And then, the multi-scale features are input into the multi-scale global features learning with channel attention (MSCA) module to obtain the global features. Besides, the multi-scale features are input into the dense cross-scale attention (DCSA) module to learn the multi-level cross-scale features. Finally, the outputs of these two modules are considered for computing the final class score. Experimental results obtained from the three public datasets indicate that the proposed method surpasses other remote sensing classification methods.
Mapping Indian Rice Yields Through Statistical Modeling Using Satellite and Climatic Data

Rising temperatures and poor field-management practices threaten meeting future food demands. To address these issues, this research maps rice yields in Punjab, Haryana, and West Bengal, India, from 2001 to 2021. A multiple linear regression model was trained using 117 climate and satellite data predictors to estimate yields. With a reasonable accuracy (R2 = 0.61), the simulated rice yields have increased by 30% in 2021 since 2001. This increase was controlled by vapor pressure deficit, minimum temperature during the sowing month (June) of the Kharif season (June to October), and the normalized difference vegetation index (NDVI) during the harvest stage (October) of the Kharif season. The outputs from the study may help farmers and policymakers to plan a sustainable agricultural future.
Requirements for 3D Models to be Used in SAR Image Simulation

The simulation of Synthetic Aperture Radar (SAR) images plays an important role for many applications such as Automatic Target Recognition, damage detection or for the in-detail analysis of signatures found in real SAR images. The need for creating synthetic SAR images has increased with the advent of Convolutional Neural Networks (CNNs), since, in contrast to the Electro-Optical domain, only a few very specialized data sets of SAR images exist that could be used for training CNNs. Today, several academic and commercial SAR simulators exist. For all of these, the foundation for the simulation is a 3D model of the scene or object to be simulated. Such 3D models exist in large quantities and can be obtained for virtually any object imaginable. However, these models were not specifically designed for SAR simulation and thus the question arises whether they are suitable for the task. In this paper, several aspects of 3D models are highlighted that should be considered when assessing them for SAR simulation.
A Fast Super-Resolution Imaging Method via Subspace Detection and Near-Field Phase Compensation for mmW Automotive Radar

Millimeter-wave (mmW) automotive radar imaging technology has great potential in advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular (azimuth) resolution for automotive radar with limited aperture. However, these super-resolution methods have high computational complexity meanwhile have poor imaging performance in single-snapshot. In this paper, combined with CS based on ℓ1-norm regularization, we proposed a fast super-resolution imaging method via subspace detection and near-field phase compensation. Frist, the range-pulse-compression (RPC) data in the near and far field is formed by using range FFT. Then, the subspace detection is presented to reduce the size of the measurement matrix by using angle FFT to obtain the potential angle subspace of the target with all RPC data. After, the near-field phase compensation is utilized to make the measurement matrix applicable to all RPC data. Finally, the iterative shrinkage-thresholding algorithm (ISTA) algorithm is utilized to form the super-resolution images based on the measurement matrices and all RPC data. The simulation results show the proposed method can significantly improve imaging resolution with lower computational complexity than other imaging methods.
Deep Learning for Unsupervised Denoising of Radar Sounder Data

Analyzing radar sounder (RS) profiles allows the retrieval of critical information on subsurface geology. However, radar-grams suffer from several noise contributions, adversely affecting the data quality and reliability. In the remote sensing literature, there are no methods for denoising radargrams, and those for optical data denoising and SAR and GPR data de-speckling are based on assumptions that are not valid in the RS domain. This paper analyses the statistical distributions of the noisy contributions in radargrams at different levels of processing. It proposes a novel method to denoise complex raw radargrams using a generative network (diffusion probabilistic model) that learns the noise statistical properties. The model is iteratively trained to learn the information loss as a function of the noise level increment in the data. By reversing the process, the network estimates the noise statistical properties and denoises unseen radargrams. The method has been successfully validated on the raw Experiment Data Record (EDR) radargrams of Mars that the Shallow Radar (SHARAD) acquired.
Polyhedron-Based Graph Neural Network for Compact Building Model Reconstruction

Three-dimensional (3D) building models play a crucial role in shaping digital twin cities and enabling a wide range of urban applications. However, one challenge remains in obtaining a compact representation of buildings from remote sensing. This paper introduces a novel deep learning approach to reconstructing polygonal building models from LiDAR point clouds. Our method leverages a graph neural network to assemble the polyhedra generated through space partitioning, thereby formulating building surface reconstruction as a graph node classification problem. To facilitate network training, we construct a synthetic dataset by simulating aerial LiDAR point clouds on building surface meshes. Experimental results demonstrate the effectiveness of our method, achieving a polyhedral classification accuracy of 96.4%. Moreover, our approach offers high efficiency and interpretability through end-to-end optimization.
A Novel Change Point Detection Method for Data Cubes of Satellite Image Time Series

Following the launch of the Copernicus Sentinels, which has enabled the free access to petabytes of satellite data, change point analysis has caught the attention of the remote sensing community. In fact, the exploitation of satellite image time series has a number of advantages over the use of an image pair, as it allows a better understanding of how the process under study is evolving. Although this is a well-known area of research that spans different application domains, the majority of the change point detection methods have been designed for the analysis of univariate signal, and only a few of them can be used to process multidimensional data. In this paper, we present a novel change point detection method based on the combination of wavelets and mathematical morphology for the analysis of data cubes of satellite image time series that allows the user to reduce the data dimensionality at the input level. We conducted a preliminary performance assessment on 50 sites in Belgium using up to 5 different input features derived from Sentinel-1 and Sentinel-2 data.
Meta Learning-Based Approach for Few-Shot Target Recognition in ISAR Images

Rapidly evolving deep learning methods have yielded remarkable performance in Inverse Synthetic Aperture Radar (ISAR) target recognition. However, training deep neural networks often requires large-scale annotated datasets. Due to the scarcity of ISAR images, it is challenging to obtain sufficient well-labeled ISAR datasets. Therefore, this paper considers Few-Shot scenarios and investigates the fast learning and generalization of the model via a Meta-Learning framework. The simulated experimental results illustrate that the Meta-Learning model presented in this paper outperforms traditional Machine Learning method K-Nearest Neighbor (KNN) in terms of testing accuracy, achieving a 72.79% improvement in 5-way 6-shot tasks. In addition, we propose Learning Gain as a criterion to measure the learning ability of the model.
Scanning Radar Super-Resolution Imaging of High-Speed Platform by Pattern Distorted Complex Convolution Model

Scanning radar (SR) super-resolution imaging has been widely reported recently. However, most of the existing methods are based on the amplitude convolution relationship between the reflectivity function and the antenna pattern, realizing super-resolution imaging through deconvolution. When the radar platform moves fast, Doppler phase and pattern distortion caused by the movement destroys the above convolution relation and decreases the super-resolution performance. In this paper, we proposed a pattern distorted complex convolution model to describe SR super-resolution imaging under high-speed platform. Simulations show that the proposed model is more suitable for SR super-resolution imaging under high speed platform.
Soil Moisture Measurement Using Twin-Lead Ladder Line

This paper describes a new method of measuring soil moisture via its dielectric properties using a parallel twin lead ladder line. Using a Vector Network Analyzer (VNA) to measure the reflection coefficient of the transmission line which is then used to find the true permittivity of the soil. An experiment is conducted by burying a 450 Ohm ladder line where in which the result is compared to the method derived by other method where it shows good correlation at moisture levels below 25%.
High-Resolution Radio Frequency Interference Detection in Microwave Radiometry Using Deep Learning

The success of microwave radiometry depends on how accurately it can measure the natural emission of the Earth without the effects of unwanted signals. The consequence of unwanted signals in radiometers is known as radio frequency interference (RFI). The high intensity of these corrupted signals, along with wider bandwidth and longer duration, may jeopardize the overall success of a mission. These reasons resulted in a need for a robust RFI detection algorithm that will enable the mitigation of the contaminated portions of the measurements. Attributes related to RFI could be very dynamic, making it very difficult to detect with a particular algorithm. To address this issue, deep learning (DL) could be an attractive solution to detect RFI with the help of time-frequency analysis, i.e., spectrograms of the received measurement. This study aims to detect and localize RFI in a particular time-frequency bin of spectrograms with the help of DL to retrieve the non-contaminated portion of the measurements.
GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data

Glacier mapping is essential for studying and monitoring the impacts of climate change. However, several challenges such as debris-covered ice and highly variable landscapes across glacierized regions worldwide complicate large-scale glacier mapping in a fully-automated manner. This work presents a novel hybrid CNN-transformer model (GlaViTU) for multi-regional glacier mapping. Our model outperforms three baseline models—SETR-B/16, ResU-Net and TransU-Net—achieving a higher mean IoU of 0.875 and demonstrates better generalization ability. The proposed model is also parameter-efficient, with approximately 10 and 3 times fewer parameters than SETR-B/16 and ResU-Net, respectively. Our results provide a solid foundation for future studies on the application of deep learning methods for global glacier mapping. To facilitate reproducibility, we have shared our data set, codebase and pretrained models on GitHub at https://github.com/konstantin-a-maslov/GlaViTU-IGARSS2023.
Detection of Tea Box Orientations in Retail Shelves Images

This paper presents a new deep learning-based method for the detection of tea box orientations in shelf images. Due to the lack of data, we had to generate synthetic datasets using the Blender rendering framework. This is done by first hand-crafting a 3D model of a retail shelf which is then stocked with a selection of various tea boxes. These 3D models of the tea boxes are obtained by the process of $\mathbf{3D}$ scanning and uploading the scans to the Blender rendering software. Next, the behavior of customers is simulated using Markov chains, and therefore some tea boxes are removed from the shelf. While stocking the tea boxes they are randomly oriented for the sake of dataset variance maximization. Finally, 10000 virtual retail scenes are rendered and annotated in three different annotation sets. We train the YOLOv5 object detection model on the synthetic datasets and achieve an impressive result of up to 84.2% mean average precision. This experimental result shows high promise for future research in this field of object pose estimation in dense scene images.
Multiview Siamese Collaborative Network for Hyperspectral Image Unmixing

Existing hyperspectral image unmixing methods acquire information from single-view, and therefore can hardly make full use of diverse spectral information, and the learning of space and spectrum is often relatively independent and cannot be combined effectively. In order to solve the problem of insufficient feature representation caused by the single-view spectral information and the lack of close relationship between spatial and spectral learning, we introduce the idea of multiview data construction, which divides the spectral bands into different views for multiview learning. In addition, we proposed a spatial-spectral siamese network. Deep collaborative learning is used to construct an unmixing model by combining multiview representation and the siamese network. Experimental results on the Jasper Ridge and Urban datasets demonstrate the effectiveness of the proposed method.
Informative Earth Observation Variables for Cotton Yield Prediction Using Explainable Boosting Machine

Cotton, a vital crop in the global textile industry, faces challenges from climate and ecosystem changes. Accurate cotton yield prediction is crucial for the economy and environmental sustainability, and it requires a deep understanding of the complex relationship between its parameters and yield. To achieve this, a comprehensive approach integrating climatic factors, soil parameters, and biophysical parameters observed through high-resolution remote sensing satellites was employed. This study utilized a multisource dataset to develop a predictive model for cotton yield over Turkiye, allowing accurate yield estimation and understanding the impact of the Earth Observation (EO)-based yield predictors on the model. Specifically, we utilized the Explainable Boosting Machine (EBM) algorithm to model and predict cotton yield while offering insights into selecting EO predictors. Additionally, we conducted a performance evaluation of our proposed approach in comparison to popular boosting-based algorithms like eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and Light gradient boosting (Light-GBM).
Developing a Framework for Rapid Collapsed Building Mapping Using Satellite Imagery and Deep Learning Models

After a major disaster, a rapid assessment of building damage is highly required for emergency response and prompt recovery. Remote sensing technologies have been widely applied for building damage mapping. Combining machine-learning algorithms (e.g., deep learning) and satellite images has recently demonstrated success in boosting damage recognition methods. Although previous techniques have shown great success, they primarily adopt supervised settings, often requiring a minimum number of training samples to achieve acceptable accuracy. Moreover, previous methods also are developed for specific target areas, which makes it challenging to apply them to other regions in case of future disasters. This paper presents a novel unsupervised approach for building damage mapping, focusing on collapsed structures, using modern convolutional neural network (CNN) models and high-resolution remote sensing imagery. We apply our mapping framework to revise the building damage following the 2007 Peru-Pisco Earthquake and the recent 2023 Turkey and Syria Earthquakes.
Feasibility Study to Detect Floating Debris by Hyperspectral Mission Using Onboard AI

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide routine hyperspectral observations over the land and coastal zone through the Copernicus Program in support of EU- and related policies for the management of natural resources, assets and benefits [1] , [2] . CHIME is an operational mission covering land and coastal areas which already uses of well-established ground processing routines in place that is independent of the AI unit we are discussing here. Areas outside the nominal observation scenario call for new methodologies such as on-board processing which will bring advantages including: fast processing of data, reducing the amount of data to be down-linked especially over ocean that CHIME sensor will be on but there is no regular acquiring plan, capability of on demand request handling. However there are some drawbacks i.e. complex validation and setting up update routines. Therefore, there has been an assessment going on to have an artificial intelligence unit on board to process acquired data and select only part of the data that contains desired targets. Based on the importance of detecting floating plastic debris for applications such as fishing farms preservation, navigation, tourism, etc., in marine environment, this application has been selected to serve as one of the test cases to evaluate Artificial Intelligence (AI) on board [3] .
Detecting Precursor Onset of Acceleration Based on Temporal Saliency and Non-Parametric Spatial Clustering of Ipta Displacements

Geotechnical land-deformation events, such as slope failures associated with landslides at mining sites, often occur without much apparent warning, thus causing loss of lives and property. Satellite-based multi-temporal interferometric synthetic aperture radar (MT-InSAR) approaches, such as interferometric point target analysis (IPTA) are commonly used to study the spatio-temporal evolution of displacement velocities in landslide-prone regions. These can be utilized in displacement-based time of failure (ToF) forecasting methods such as ‘inverse velocity’ [1], [2] to provide early alerts. However, forecast accuracy is dependent on the correct identification of the precursor date corresponding to the onset of acceleration (OoA). This is further hindered by the noise in the MT-InSAR time series, that leads to a high false-alarm rate. To provide failure alerts for mining operations, we investigated a methodology for detecting OoA precursor robust to temporal and spatial noise. OoA saliency detection, an antecedent to the inverse velocity, performs fast Fourier transform (FFT) based spectral residual (SR) to identify anomalous parts of the original sequence. This framework shall improve the application of ToF prediction in the operational monitoring of mine slopes. We evaluated its performance in the Brumhadino case while showing the effect of the existing smoothing-based velocity calculations on OoA and ToF predictions.
Berman Codes: A Generalization of Reed–Muller Codes That Achieve BEC Capacity

We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as <inline-formula> <tex-math notation="LaTeX">$\mathscr {C}_{n}(r,m)$ </tex-math></inline-formula>, and their duals are denoted as <inline-formula> <tex-math notation="LaTeX">$\mathscr {B}_{n}(r,m)$ </tex-math></inline-formula>. The length of these codes is <inline-formula> <tex-math notation="LaTeX">$n^{m}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n \geq 2$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is their ‘order’. When <inline-formula> <tex-math notation="LaTeX">$n=2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\mathscr {C}_{n}(r,m)$ </tex-math></inline-formula> is the RM code of order <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> and length <inline-formula> <tex-math notation="LaTeX">$2^{m}$ </tex-math></inline-formula>. The special case of these codes corresponding to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> being an odd prime was studied by Berman (1967) and Blackmore and Norton (2001). Following the terminology introduced by Blackmore and Norton, we refer to <inline-formula> <tex-math notation="LaTeX">$\mathscr {B}_{n}(r,m)$ </tex-math></inline-formula> as the Berman code and <inline-formula> <tex-math notation="LaTeX">$\mathscr {C}_{n}(r,m)$ </tex-math></inline-formula> as the dual Berman code. We identify these codes using a recursive Plotkin-like construction, and we show that these codes have a rich automorphism group, they are generated by the minimum weight codewords, and that they can be decoded up to half the minimum distance efficiently. Using a result of Kumar et al. (2016), we show that these codes achieve the capacity of the binary erasure channel (BEC) under bit-MAP decoding. Furthermore, except double transitivity, they satisfy all the code properties used by Reeves and Pfister to show that RM codes achieve the capacity of binary-input memoryless symmetric channels. Finally, when <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is odd, we identify a large class of abelian codes that includes <inline-formula> <tex-math notation="LaTeX">$\mathscr {B}_{n}(r,m)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathscr {C}_{n}(r,m)$ </tex-math></inline-formula> and which achieves BEC capacity.
Hay meadows with Trisetum flavescens in Bulgaria: syntaxonomy and implications for nature conservation

Trisetum flavescens has relatively wide distribution in the country, but seldom dominates in the communities. It has not been studied syntaxonomically so far. On the basis of 37 releves the current analysis reveals that T. flavescens communities are related to the Arrhenatheretum elatioris association. While otherwise similar to the Central European Arrhenatheretum elatioris, a significant portion of the species composition belongs to the Balkan floristic elements. Additionally, the presence of many south affiliated species is observed. This finding ascertains a Central-Balkan variant of the association which has not been reported so far. Some of the ecological peculiarities of the studied sites in the Rhodopes are very similar to those of Triseto-Polygonion in Central Europe, but we have failed to find sufficient proofs for the presence of the alliance in Bulgaria. The studied vegetation has high floristic diversity. It is maintained as hay meadows. The conservation value of the Arrhenathreretum elatioris merits protection under the Habitats Directive by means of an extension of the definition of Habitat 6520 to include Arrhenatherion as well.