Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended Alpha Approximation Method for the Retrieval of Soil Moisture Under Dynamic Vegetation by Multi-Incidence Angle Sentinel-1

The retrieval of (almost) daily soil moisture from C-band Sentinel-1 Synthetic Aperture Radar (SAR) records is still influenced by incidence angle effects and vegetation dynamics. In this study we present a method to reduce the effects of both methods on the alpha approximation methods, a time series approach assuming changes in backscattering signals between two consecutive observations are related to a change in soil moisture. By implementing a Fourier series for incidence angle normalization and a linear regression to co-polarized backscatter for a vegetation adaption, the alpha approximation method has been extended to gain high temporal resolution soil moisture time series. The approach was tested in the Rur catchment, Germany and the Apulian Tavoliere, Italy.
DeepTrCy: Life Stage Identification of Satellite Tropical Cyclone Images

Satellite imagery with multiple spectral bands plays a crucial role in the analysis and prediction of atmospheric phenomena. Tracking tropical cyclones, i.e typhoons, are easily monitored by infrared bands with predefined typhoon life stages. In the life cycle of such typhoons, they typically exhibit formation of a few clouds to fully developed spiral clouds with a clear eye. The life stage is classified by analyzing the maximum sustained winds at a distance from the center of the eye. The lowest pressure is also used to characterize the magnitude of typhoons. Moreover, apparent typhoon shapes can be roughly classified into different life stages according to experts. However, this limits other applications due to auto-operational tasks. Therefore, this paper proposes a Deep Learning based satellite image classification for identifying the life stage of tropical cyclones, namely, DeepTrCy. Due to the limited training on past typhoon images, applying data augmentation by introducing a fluid-like noise, i.e., Perlin noise, is firstly proposed. Using different DL models by introducing different augmentation techniques like Perlin noise levels, and different input data like optical flow, results are compared to that of CNN and transformer architectures. The proposed DeepTrCy has demonstrated high accuracy on identifying tropical cyclone stages from the several experiments.
A Deep Active Learning Framework for Semantic Labeling of Point Clouds

Point clouds semantic labeling is an important task in 3D computer vision. Current major researches focus on fully supervised learning. However, point-by-point manual annotations are expensive and time-consuming. To this end, we propose a general point clouds deep active learning framework to ease the annotation burden for researchers. In this work, we propose a conditional random field (CRF) based pseudo labels generation to provide more supervised information for deep neural network (DNN) and employ Golden Loss Correction (GLC) to correct pseudo labeled data training loss. Finally, we propose the Modified Margin acquisition function which can select the most valuable points for labeling. We demonstrate the improvements provided by our proposed method on the S3DIS benchmark.
Using Sentinel-2 and Stacking Regressors for Forest Height Estimation

The climate change impacts can also be seen in the growing number of wildfires. Consequently, forest management and the updating of forest inventories become more important in the wildfires’ avoidance. Measuring the Forest Height (FH) is an important activity in forests monitoring, since the FH can serve as a proxy variable of other parameters, as the aboveground biomass. Normally, FH is mapped through field campaigns or airborne laser scanning missions. However, these approaches do not offer the scalability needed and they are expensive. Therefore, multispectral data from Remote Sensing can be used for producing regional maps of FH. Here it is proposed a regionally calibrated Regression Methodology that uses multispectral data from Sentinel-2 and a Stacking Regressor for mapping the FH in Mediterranean forests. For a total of 17 regions across Portugal, Spain, and California, a R2 between 43.71% and 72.85% and a RMSE between 0.85m and 4.03m.
Downward-Looking Ship Target Tracking Based on Rotated DSST Algorithm

For ship targets in down-sight conditions, the aspect ratio is uneven. Therefore, horizontal box labeling can lead to the interference of too much background information, making the target modeling inaccurate. To address this issue, a Rotated Discriminative Scale Space Tracker (roDSST) based on DSST is proposed. Firstly, the sampling method has been refined. The rotated target area is sampled using bilinear interpolation to reduce background interference and avoid quantization errors. Secondly, an angle filter is introduced to predict the rotation angle of the bounding box. Finally, the template update mechanism is improved by always retaining a certain weight of the initial template. The experimental results show that roDSST has superior performance in tracking accuracy and robustness on downward-looking ship sequences.
HGR Maximal Correlation Augmented Cross-Modal Remote Sensing Retrieval

Most existing methods for cross-modal content-based remote sensing image retrieval (CM-CBRSIR) have only focused on implementations by optimizing the projected features in a common space under the Euclidean distance criterion. In this work, to better bridge the heterogeneity gap caused by the modality difference, we propose a Hirschfeld–Gebelein–Rényi (HGR) maximal correlation augmented CM-CBRSIR method by utilizing the HGR maximal correlation between different modalities. Except for optimizing the projected features under the Euclidean distance constraints, another feature projection, which carries the information of the HGR maximal correlation, is learned during the training phase. In the retrieval phase, we combine the information learned by the Euclidean distance criterion and HGR maximal correlation based on the Dempster–Shafer (DS) evidence theory. Experimental results show that the proposed method outperforms the existing state-of-the-art methods.1
SODIUM HYPOCHLORITE AND ALTERNATIVE SANITIZERS FOR SANITATION OF SIMULATED PACKINGHOUSE WATER SYSTEMS

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SODIUM HYPOCHLORITE AND ALTERNATIVE SANITIZERS FOR SANITATION OF SIMULATED PACKINGHOUSE WATER SYSTEMS By Michael Mahovic December 2004 Chair: Jerry A. Bartz Major Department: Plant Pathology Tomatoes are an important economic product of the United States, with Florida production accounting for 47% of the total fresh market winter crop. Microbial contamination can lead to decays or consumer health hazards. Combating either type of contamination in water handling systems currently relies on chlorination; experience has shown that potential hazards may not be controlled in this system. This research involves determining the capability of plant pathogens to survive biocidal treatments and screening of some potential alternative treatments to chlorination. Determining a biocide’s usefulness should be based on product availability, ease of use, known biocidal activity and safety. Five biocides were selected for testing here: chlorine (from sodium hypochlorite), acidified sodium chlorite (ASC); peroxyacetic acid (PAA); and chlorine dioxide (ClO2), as an aqueous solution (ClO2(aq)) or a vapor (ClO2(g)). The biocides were tested for efficacy against three common postharvest pathogens of tomatoes: the soft rot bacterium, Erwinia carotovora subsp. carotovora; the
Seismic Data Random Noise Attenuation Using Visible Blind Spot Self-Supervised Learning

Due to various reasons, seismic data are often inevitably affected by noise. Therefore, random noise suppression of seismic data is a key step for seismic data processing workflow. Recently, deep learning method has performed well in seismic data denoising. In this study, we propose a self-supervised deep learning seismic data noise attenuation method. We introduced an effective Blind2Unblind (B2U) denoising framework, which can complete denoising using only a single noisy seismic data. Use a mask mapper with global awareness, which can sample all pixels at the blind spots on noisy data and map them to a same channel. At the same time, a re-visible loss function is used to train the network, which can optimize all blind spots, mitigating the information loss and retaining more details of geological structure. The denoising experiments on synthetic and field data show that our method has achieved superior results compared with previous work.
Simultaneous Retrieval of Land Surface Temperature and Emissivity from Chinese Geostationary Satellite Fengyun-4B Image

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.
Influence of Data Cleaning Techniques on Sub-Field Yield Predictions

Modern combine harvesters can collect geo-located real-time yield measurement while harvesting. This data can be used to train Machine Learning models that predict the yield at sub-field level based on remote sensing input data. The performance of these models is, however, highly dependent on the quality of the yield data. It is therefore important to develop automatic cleaning techniques to correct for common errors in combine harvester yield maps. In this work, we compare different combinations of data cleaning techniques by evaluating their impact on the yield-prediction model performance at field and sub-field level. Our findings indicate that basic cleaning techniques such as absolute thresholds are sufficient at the field level, whereas the performance at the sub-field level is enhanced through the utilization of more intricate statistical cleaning methods.
Light-Weighted Explainable Dual Transformer Network for Hyperspectral Image Classification

Although light-weighted explainable deep learning techniques are critical for operational hyperspectral image (HSI) classification, it is very challenging to achieve these techniques due to difficulties to deal with the spatial-spectral complexity and coupling effect in HSI. Leveraging the excellent feature learning capability of the attention mechanism, this paper presents a spatial-spectral dual transformer (SSDT) network that decomposes the conventional spatial-spectral transformer operation into a spatial transformer and a spectral transformer, which not only reduce the model complexity, but also allows the use of self-attention to explain feature relevance. The proposed approach is tested on some benchmark HSI scenes and the results demonstrate that the proposed dual transformer network not only achieves new state-of-the-art performance due to its excellent feature extraction capability, but also enables the analysis and visualization of feature importance and decision making process.
A Deep Learning Sea Ice Forecasting Model Considering Sea Ice Change Characteristics

This paper presents a novel deep learning model for short-term sea ice forecasting that accounts for the effects of teleconnection and delay phenomena on sea ice changes. The proposed model is a convolutional neural network with an embedded attention mechanism that takes into account the past 21 days of Arctic environmental element data as input. It is capable of providing daily forecasts of sea ice concentration in the Arctic core area for the next 49 days. Notably, the model outperforms both the deep learning sea ice forecast model Icenet and the numerical sea ice forecasting models ArcIOPS and SEAS5, demonstrating its superior short-term forecasting capability for sea ice.
Correlated NMS: Establishing Correlations Between Dense Predictions of Remote Sensing Images

Object detection is an important task for remote sensing image analysis, which aims to identify and locate objects within captured remote sensing images. Several object detection methods have been proposed, among which Non-maximum suppression (NMS) is an essential ingredient of these methods. Although simple and useful for object detection, the performance of detection methods with NMS will degeneradte when the objects within remote sensing images are dense. One of the main reasons is the presence of severe occlusion in some remote sensing images, which can easily mislead NMS to suppress the nearby candidate box of different object from its central box. To address this problem effectively, we propose to measure the correlation between the candidate box and the central box, from which if these two boxes come from the same object can be estimated. Building on this idea, we propose Correlated NMS, which can adaptively adjust the suppression threshold between the candidate box and its central box based on whether they tend to represent the same object. Experimental results demonstrate the effectiveness of the proposed method.
Research on Fast Detection Method of Wind Turbine in Remote Sensing Image Land Area Based on Yolo

With the development of the social economy, wind turbines are taking up a larger and larger share of new energy sources. The detection of the number and spatial distribution of wind turbines in remotely sensed images holds great scientific significance. Wind turbines are difficult to identify in remote sensing images therefore, a fast detection method based on deep learning is proposed. First, we extract potential wind turbine candidate regions from wind speed, slope, and land use data. Second, the YOLO v5 model was trained using our labeled wind turbine detection dataset. Finally, the images of the candidate regions were used for wind turbine detection using the trained optimal model. The proposed method was demonstrated to have a recall of 94.87% and an accuracy of 82.04% through the experimental results. The proposed method for wind turbine detection is not only reasonable and effective but also offers a heightened level of efficiency.
Multi-Spectral Band Selection and Spatial Explanations Using XAI Algorithms in Remote Sensing Applications

This work proposes an interpretable Deep Learning framework utilizing Vision Transformers (ViT) for the classification of remote sensing images into land use and land cover (LULC) classes. It uses the Shapley Additive Explanations (SHAP) values to achieve two-stage explanations: 1) bandwise feature importance per class, showing which band assists the prediction of each class and 2) spatial-wise feature understanding, explaining which embedded patches per band affected the network's performance. Experimental results on the EuroSAT dataset demonstrate the ViT's accurate classification with an overall accuracy 96.86 %, offering improved results when compared to popular CNN models. Heatmaps in each one of the dataset's existing classes highlight the effectiveness of the proposed framework in the band explanation and the feature importance.
New Advances of the P-SBAS Approach for the Generation of SAOCOM-1 L-band DInSAR Time-Series

We present in this work some advances of the Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach, allowing us to effectively process the recently available L-band SAR images acquired by the Argentinian SAOCOM-1 constellation. This activity has been carried out within the DInSAR-3M project, co-funded by the Italian Space Agency, which is aimed to develop technical solutions for the joint exploitation of multi-frequency (X-, C- and L- band) and multi-platform (spaceborne and airborne) DInSAR measurements. The main goal is the generation of surface deformation time series and corresponding mean displacement velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena.The assessment of the developed advances has been carried out by processing several SAOCOM-1 SAR datasets acquired over Italy between 2019 and 2022.
Review on Traffic Flow Prediction Method Based on Neural Network

Increasing frequency of traffic congestion, traffic accident and other abnormal situations have become a central issue today. In this case, seeking for an accurate approach to make traffic flow prediction of high credibility is important. However, traffic prediction is very difficult due to the amount of traffic flow data, its temporal uncertainty and periodicity, and the spatial correlation between road network nodes. The mainstream methods today are data-driven methods, among which the graph neural network method is widely used. In this paper, several graph neural network methods and several non-graph neural network methods are introduced, and the characteristics, outstanding parts, differences and defects of these two types of methods are analyzed. The comparision is based on the structure of the two types of models. It is concluded that the graph convolutional neural network can more accurately extract the spatial dependencies and thus predict more accurately.
Role of Ku-band (13.5 GHz) based Scatterometer Satellite (SCATSAT-1) in Cryospheric applications

As an active microwave remote sensing sensor, Scatterometer measures the earth’s surface in the form of a radar cross-section. In the past few decades, several scatterometers were launched into space and found numerous applications in earth observations. The scatterometer satellite (SCATSAT-1) is one of the ISRO’s (Indian Space Research Organization) scatterometer based on Ku-band (13.5 GHz) which recently completed its mission in 2021. It was launched on 26th September 2016 from the Satish Dhawan Space Centre, Sriharikota, India. Due to the measurements over the land surface and non-land surfaces, the major scientific domains of scatterometer application may include oceanography, hydrology, cryosphere, and agriculture. This article highlights the major milestones of the SCATSAT-1 in the field cryosphere which were achieved in nearly five years of its operation. Due to its measurement over the land surface, snow cover monitoring over the Himalayan region is one of the major applications of SCATSAT-1. It also took measurements over the wide coverage of polar regions which plays an important role in the monitoring of sea-ice extent or tracking large icebergs and hence, the particular interest for climate change research communities. This article also highlights the potential applications of recently launched (26th November 2022) oceansat-3 (a successor of OCSCAT and SCATSAT-1) in the field of the cryosphere.
Neural Network and Data Structure for Uncertainty Detection within GIS

This paper takes place at the crossing of Deep Learning [1] , Knowledge Representation [2] , Fuzzy Modeling [3] , [4] and Geographic Information Systems (GIS) [5] . Indeed, it presents a new way to detect, localize and represent uncertain knowledge within a GIS ( [6] , [7] , [8] ). Uncertainty can be interpreted by inaccurate spatial data linked to positioning or labeling errors. It could also be due to complex areas where a precise labeling and positioning is quiet difficult. This is particularly the case for gradual phenomena such as forest ecosystems, or the diffusion of particles into the air or water. We have applied this method to detect the environmental impact of agriculture in Guadeloupe, a french west indies island.
Semi-Supervised Satellite Image Segmentation Using Spatial and Temporally Informed Poisson Learning

Satellite image segmentation is crucial in various fields, but acquiring a large amount of labeled data can be challenging. Often, remote sensing images are unlabeled due to the high cost of manual labeling, which hampers training effectiveness and generalization. To address this issue, we propose a spatially and temporally informed graph-based semi-supervised learning approach for satellite image segmentation based on Poisson learning. The main difference to traditional Poisson learning is that our distance function that we use to compute similarity between pixels considers spectral, spatial, and temporal information. Experimental results on the Sentinel-2 time series demonstrate that our approach outperforms other traditional approaches, achieving robust performance in remote sensing image segmentation, especially at a very low label rate.
Monitoring Forest Biomass Dynamics in the Laurentides Reserve, Canada, Using Lidar Data and Radar Imagery

We designed a multisource approach for estimating forest above ground biomass (AGB) for the Laurentides Wildlife reserve in Canada, using airborne and spaceborne LiDAR data and radar imagery. Our workflow used detailed forestry inventory data as the starting point of the analysis, and remote sensing data to extend the spatial and temporal coverage of the biomass estimation. We estimated an average value of 35 Mg/ha of biomass for individual forest stands in the reserve, and an increase in average forest stand biomass between 2015 and 2022 of 3 Mg/ha, which indicates that the Laurentides reserve is overall a stable sink of Carbon. Our results show the benefits of using multisource remote sensing data for producing multitemporal and spatially explicit AGB maps.
Calibration of Geolocation Error of ICESAT-2/Atlas Laser Data Using Crossovers

The photon-counting Advanced Terrain Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides high-density laser point data on the Earth's surface. Previous calibration methods for geolocation errors required calibration fields or large-scale reference data. In this study, a geolocation error calibration method based on crossovers is proposed, which mainly relies on consistency conditions at trajectory crossovers. The results show that the proposed method can be used to estimate the geolocation error of laser data, and the total geolocation error is 3.57m, which is basically consistent with the result (2.84m) of the traditional terrain matching method. Furthermore, after calibrating for geolocation errors, the elevation accuracy of the laser data improved from 0.43m to 0.34m. In summary, the crossovers show potential in calibrating the geolocation errors of spaceborne photon-counting laser altimeters.