Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Reconstruction Algorithm for Tunable Detectors: A Learning Based Approach

Hyperspectral imaging, crucial for detecting and classifying earth’s surface elements, often requires complex optical equipment susceptible to environmental perturbations. A learning-based approach utilizing smart spectral sensors that considerably reduces hardware complexity while maintaining good resolution in spectral regions of interest is proposed in this paper. This method capitalizes on the spectral redundancy inherent in spectral sensors that have poor spectral resolution albeit the wavelength at which their responsivity curve peaks is continuously tunable. Central to our approach is the optimized superposition of photocurrents, corresponding to a family of responsivities that are tuned judiciously to appropriate wavelengths, along with their pairwise correlations, to render improved resolution spectrometry and enable continuously tunable sensing in spectral domain. The weights for this superposition are learnable parameters derived through a learning process utilizing a comprehensive library of spectra corresponding to various materials and filters. Through learning from this spectra library, the algorithm finds an optimal set of weights that can reconstruct any realistic spectrum as similar as possible. By employing a stochastic gradient-descent method to minimize mean-square error, the weights are generated to enable high-resolution estimation of the spectrum of the object being sensed. The method was applied to quantum-dot-in-a-well electrically tunable infrared photodetectors to estimate various spectra from the ECOSTRESS Spectral Library after being trained with 1,855,000 sample spectra. The method demonstrated a 17-fold improvement in target spectral estimation accuracy compared to prior methods. Additionally, when integrated with an Artificial Neural Network, the method achieved 96.38% accuracy in material classification. This innovation promises significant advances in high-resolution spectrometry and accurate material classification in various practical applications.
New Informed Linear Mixing Model And NMF-Based Unmixing Method Addressing Spectral Variability With An Application To Mineral Detection And Mapping Using Prisma Hyperspectral Remote Sensing Data

In these investigations, a new informed linear mixing model addressing spectral variability with an associated informed penalized hyperspectral unmixing algorithm is first proposed. This algorithm, which optimizes a new cost function with original iterative and multiplicative update rules, is based on nonnegative matrix factorization. Then, this algorithm is used for detecting and mapping several mineral deposits in the Algerian Central Hoggar. This method unmixes the considered PRISMA hyperspectral remote sensing data by exploiting known spectra of some minerals of interest. Obtained abundance fraction maps are then used to establish a classification map of the investigated area, with the considered mineral deposit classes, at a finer spatial resolution.
Groundwater pressure changes in Central Japan induced by the 2011 off the Pacific coast of Tohoku Earthquake

In the 2011 off the Pacific coast of Tohoku Earthquake, groundwater pressure changes were observed in and around the Mizunami Underground Research Laboratory (MIU) in Central Japan, where two vertical shafts and horizontal research galleries are excavated in the granitic rock mass. Coseismic changes of groundwater pressure are believed to correspond to crustal dilation/contraction induced by earthquakes. In this study we calculated volumetric strain changes due to the Tohoku Earthquake based on previously reported fault slip models. The calculation indicates approximately 2 × 10−7 of dilational strain around the MIU. Based on the strain sensitivities calculated from tidal responses at the monitoring boreholes, the dilation corresponds to drawdowns of several tens of centimeters, and is almost the same as the drawdown observed in the boreholes at distances greater than 1 km from the MIU. In contrast, rapid elevation of groundwater pressures associated with the earthquake was observed in the boreholes within the 500 m vicinity of the MIU. The anomalous elevation is explained by a temporary recovery of the drawdown due to excavation of the shafts and a unique permeability increase induced by the coseismic dilation of heterogeneous local geological structures such as impervious faults controlling the hydrogeological environment.
Forest Vegetation Optical Depth Mapping Using GNSS Signals at SMAPVEX’22

Two intense observation periods (IOPs) are included in the Soil Moisture Active Passive (SMAP) Validation Experiment (SMAPVEX) 2022 in the temperate forests of the northeastern US (Massachusetts and New York). Because a sizable portion of the U.S. and the world have non-uniform forest cover at the SMAP resolution scale, the IOPs aim to test the SMAP retrieval in both fully wooded and partially forested instances. Destructive sampling is often used to assess the opacity of the forest canopy, which is intrusive and labor-intensive in forest characterization. To measure vegetative opacity directly utilizing widely accessible Global Navigation Satellite System (GNSS) signals, we have instead developed a GNSS Transmissometry (GNSS-T) approach from a mobile platform (such as a helmet wearable and quadruped ground robot). The created system gathers two simultaneous GNSS readings, one in the unobstructed open sky area and the other under the forest canopy. The difference between the two can yield information on forest transmissivity (water content). That can be used to test the SMAP retrieval methods over wooded areas. In this study, we have processed SMAPVEX’s IOP-1 GNSS-T data at selected sites, including GPS, GLONASS, Beidou and Galileo satellites, and generated forest transmissivity and vegetation optical depth (VOD) heatmaps averaged to different angular bins at both SMAPVEX’22 locations.
A Multi-Scale Deep Feature Learning and Semantic Enhancement Approach for Remote Sensing Scene Classification

Deep learning has made great success in remote sensing scene classification since the powerful feature representation and complex nonlinear relationship learning. However, existing methods ignore the information redundancy and semantic ambiguity among them. To cope with this problem, we propose a multi-scale deep feature learning and semantic enhancement approach (MDFL-SE). First, we employ Pyramid Convolution PyConvResNet as the backbone to extract multilayer convolutional features. Then, a progressive deep feature aggregation module (PDFA) is designed to use high-level features to guide the low-level ones to choose the discriminative features. Finally, a global multiscale semantic extraction module (GMSE) and a grouped semantic extraction module (GSE) are combined to extract the channel and spatial information of multilayer fusion features. Experiments performed on AID and NWPURESISC45 RSSC datasets demonstrate that the proposed framework can obtain outstanding performance compared with state-of-the-art approaches.
Article / Book Information

In this paper, we propose velocity pyramid for multimedia event detection. Recently, spatial pyramid matching is proposed to introduce coarse geometric information into Bag of Features framework, and is effective for static image recognition and detection. In video, not only spatial information but also temporal information, which represents its dynamic nature, is important. In order to fully utilize it, we propose velocity pyramid where video frames are divided into motional sub-regions. Our method is effective for detecting events characterized by their temporal patterns. Experiment on the dataset of MED (Multimedia Event Detection) has shown 10% improvement of performance by velocity pyramid than without this method. Further, when combined with spatial pyramid, velocity pyramid provides an extra 3% gains to the detection result.
A Multidisciplinary Approach to Assess the Kinematics of the Pisciotta Deep-Seated Gravitational Slope Deformation (Southern Italy)

Deep-Seated Gravitational Slope Deformations (DSGSD) comprise a collection of slow and complex deformational processes driven by gravity, which involve entire slopes over long time intervals [1] . These phenomena occur in various morpho-structural conditions and are characterized by typical morphological features. Even though DSGSD rarely claim lives, they can cause significant damage to infrastructures and sometimes fail catastrophically [2] .
Unsupervised Geospatial Domain Adaptation for Large-Scale Wildfire Burned Area Mapping Using Sentinel-2 MSI and Sentinel-1 SAR Data

Satellite remote sensing provides a cost-effective way for monitoring wildfires on a large scale, and the continuous observations and measurements have made remote sensing a primary source of unlabelled big data. Supervised deep learning has shown great success in various remote sensing applications, but it heavily relies on high-quality labels. However, burned area labels are only available for a small part of the world, supervised deep learning from limited labelled data has poor generalization performance across geographical regions and climate zones. Different satellite sensors represent the same physical objects in various ways, while multi-source satellite data often exhibits a combination of common and complementary information, such as optical and radar data. The common information makes it possible to exploit huge amounts of unlabelled multi-source data in model training through consistency regularization between multi-source predictions. In this work, we adopted an unsupervised geospatial domain adaptation (GDA) framework based Dual Stream U-Net model, which combines the supervised loss and unsupervised multi-modal consistency regularization to exploit both labelled and unlabelled multi-model data for model training in a semi-supervised learning manner. The experimental results demonstrate that unsupervised GDA has better generalization performance across geographical regions than fully supervised learning.
10-Meter Resolution Land Cover Classification Mapping Using Sentinel-1 & 2 and Dynamic World

The objective of this study was to develop a 10-meter resolution land cover classification map using Sentinel-1 and Sentinel-2 satellite imagery along with the Dynamic World data set. A Gradient Boosted Decision Tree (GBDT) classification was employed with reference samples. To reduce the salt-and-pepper effect, a decision fusion approach was applied to the classification probabilities obtained from the GBDT and the CNN-based classification from Dynamic World. The model’s accuracy increased when CNN-based classification probabilities were used as inputs of the GBDT classification, while the salt-and-pepper effect was reduced when a decision fusion was applied to those two probabilities. This study suggests that the traditional land cover classification approach with reference sampling can still be effective when integrating with CNN-based classification probabilities for rapid land cover classification mapping.
Sidelobe Suppression for Multichannel Forward-Looking SAR Imaging Based on Spatial Smoothing Coherence Factor

Sidelobe suppression is an important step in SAR imaging, especially for forward looking areas with poor azimuth resolution. In this paper, the signal model of multichannel forward-looking SAR imaging is realized with BP algorithm firstly, and then spatial smoothing coherence factor(SSCF) is introduced to suppress sidelobes of the imaging results. At last, simulation results are given to illustrate the effectiveness of the proposed scheme.
A New Deep Neural Network for Optical and SAR Image Fusion

Alert or monitoring, runs through thousands of years of human history. Now, with the growth of the number of satellites in orbit, hundreds of terabytes of data are transmitted from the satellite to the data center every day. How to efficiently understand the information contained in these huge data in the face of practical needs is an increasingly urgent engineering challenge. However, most current computer vision methods are used for Optical images. Due to the presence of domain gaps between optical images and SAR images ,the processing results are not ideal when Optical and SAR images are mixed. Therefore, in view of the above problems, a network model is proposed to realize the correlation between SAR images and Optical images. The model solves the problem that the imaging mechanism of SAR images differs from Optical images. The domain gaps cause SAR images are not directly used in Optical images computer vision method. This paper proposes an initial set of methods and models that have learned robust representations for Optical and SAR images dataset. So image analysts are able to interchangeably use Optical and SAR images for downstream tasks by using our models.
Research of Gearbox Failure Diagnosis Based On Nerve Network

Traditional diagnosis only focuses on signal collection and processing, and lacks effective noise processing. In order to solve this problem, the thesis studies the diagnosis parameters in frequency plane and time plane after processing on the basis of wavelet noise reduction processing. At the same time, according to the typical fault characteristics of gearbox, the BP neural network suitable for fault diagnosis is determined, and the model of BP neural network for gearbox fault diagnosis is established. The BP neural network analysis program is developed through the GUI module of MATLAB, and the energy of each frequency band obtained by wavelet packet processing is taken as the input of the neural network. The vibration data of ship windbreaker is used in the test to complete the training and establishment of BP neural network, or the relevant experimental demonstration is carried out. The experimental results indicate that this method is effective and has greater advantages than traditional methods.
Brightearth City Texturing : Faithful Procedural 3d Urban Modeling From Satellite and Ground Imagery

BrightEarth City Texturing is an automatic and resource-efficient 3D urban modeling pipeline, producing quality low-poly 3D models with high-resolution seamless textures suitable for real-time rendering. From a single satellite image it extracts building geometry with footprint polygons along with attributes such as building height, roof shape, and roof texture. If ground images are available it can also faithfully texture facades in terms of architectural style.
Tobacco Information Extraction Based on UAV High Resolution Images

Tobacco is one of the most important cash crops in China, and accurate acquisition of its planting information is of great significance for tobacco management and yield prediction. In this paper, a tobacco information extraction method based on UAV high-resolution images is proposed. The method uses the RtinaNet target detection model to achieve the identification and localization of tobacco in UAV high-resolution images, and generates tobacco coordinate information. Based on the tobacco coordinates, we designed two algorithms for extracting plant spacing, row spacing and area of tobacco fields, respectively. The experimental results showed that the accuracy of the method reached 98.49%-99.77%, 97.08%-99.98% and 94.48%-99.63% for area, row spacing and plant spacing extracted from the seven experimental tobacco fields, respectively.
Estimating NDVI from SAR Images Using Conditional Generative Adversarial Networks

The Normalized Difference Vegetation Index (NDVI), an indicator of vegetation health, is derived from the visible and near-infrared light reflected by vegetation, which can be measured with multi-spectral sensors. However, clouds can often obstruct land areas, making it challenging to obtain NDVI maps of the surface. This study explores the possibility of estimating NDVI from synthetic aperture radar (SAR) images using a Conditional Generative Adversarial Network (cGAN). Results encourage using a cGAN to address the task and provide valuable insights for future improvements.
Spatial-Preserving and Edge-Orienting High-Resolution Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD), with a view to probing surface changes between bi-temporal images, makes a spurt of progress with the continuous innovation of deep learning. However, the extraction of multi-scale features and the detection of small domain of variation as well as the detail information in RSCD task still has large development space. Besides, current existing methods mostly focus on learning regional information but pay less regard to boundary identification, which leads to inaccurate detection results. Therefore, a spatial-preserving and edge-orienting high-resolution network is proposed to address the problems. In the overall architecture, a dual-branch encoder consists of a pyramid feature extracted branch and an enhanced HR network branch is designed to extract muti-scale bi-temporal features and small change objectives, while two edge-orienting modules (EOM) are embedded in order to utilize edge prior knowledge for further improving the accuracy of change detection. Moreover, spatial-preserving module (SPM) based on the self-attention calculation in spatial dimension is applied in the pyramid part to alleviate the poor location information of the high-level features. The experimental results demonstrate that the proposed network outperforms the cited state-of-the-art methods on LEVIR change detection datasets (LEVIR-CD).
Extraction Of Small Dam Reservoirs Using A Combination Of Digital Terrain Model And Water Mask Derived From Satellite Images

Dam reservoirs account for only a small fraction of the global freshwater but they have major importance for human activities, providing water for agriculture, domestic, recreational, and industrial use, especially in a context of water scarcity induced by climate change [1] . They also have major impacts on the hydro- and ecosystems [2] . While they are subject to numerous studies across multiple disciplinary fields [3] , [4] , no exhaustive database of existing dam reservoirs is available, and the available data are limited to the largest reservoirs. Thus, there is a need for a method to automatically extract dam reservoirs from widely available data such as satellite images. This task is different from the broader water surface extraction task [5] , [6] as the goal is also to distinguish dam reservoirs from other waterbodies. Some approaches have been proposed to achieve it, for example by Van Soesbergen et al. [7] . However, this extraction task using only single images can be very difficult for very small waterbodies (below 1 hectare).
Lessons Learned From Cloudsen12 Dataset: Identifying Incorrect Annotations in Cloud Semantic Segmentation Datasets

In Earth observation, deep learning models rely heavily on comprehensive datasets for training and evaluation. However, the relevance of data quality is often underestimated, leading to subpar generalization in real-world remote sensing scenarios. This study aims to bridge this gap by proposing a straightforward method to identify critical human annotation errors in semantic segmentation datasets. The approach is based on two indices: trustworthiness and hardness. By implementing these indices, we estimate the extent of human annotation errors in CloudSEN12, a global dataset specifically designed for cloud detection in Sentinel-2 imagery. Considering only the trustworthiness index, our approach identified 1794 potential labelling errors among 10,000 image patches. Out of these, 106 were confirmed as human errors, resulting in a true positive rate of 9.86%. When this method was applied to other extensive cloud masking datasets, such as KappaSet and Sentinel-2 Cloud Mask Catalogue, it was found that over 44% of the human labels were inaccurate. These results do not imply the inferior quality of these datasets, instead, they highlight the considerable shift between the annotation protocols, making inter-dataset benchmarking exercises inequitable.
A Meta-Feature Model for Exploiting Different Regressors to Estimate Sugarcane Crop Yield

The crop yield prediction is crucial for the sugarcane grower to estimate the amount of biomass that will be harvested in decision-making for the acquisition of agricultural fertilizers and pesticides, for carrying out the harvest, and for the reform of the cane field. Usually, the features used for crop yield prediction are based on the direct observations of what occurs on the field collected by sensors or manually. But modeling the problem with new features, calculated by regressions applied to features collected from the phenomenon, can help to explore better the results that dataset retrieves. And it is possible by using these retrieves as new features to be modeled in other regressions. This article explores the viability of producing new features, called here meta-features (MF), to find better results for the sugarcane crop yield prediction. These meta-features were created from the results obtained by different regressors used to analyze which of them would present the best prediction in the original dataset. The regressions using these meta-features obtained better results in terms of ${\bar R^2}$ and errors associated with the crop yield measured on the field.
18. Innovation and Diffusion

In 1953, a young female Macaque monkey in the south of Japan washed a muddy sweet potato in a stream before eating it. This obvious improvement in food preparation was imitated quickly by other monkeys and in less than 10 years it became the norm in her immediate group; by 1983, the method had diffused completely. In 1956, the same monkey innovated again, inventing a technique in which handfuls of mixed sand and wheat grains were cast upon the sea, so that the floating cereal could be skimmed from the surface. Again, by 1983, this method of gleaning wheat had diffused almost completely throughout the local populations of Macaques.
Cellular Interactive Attention Network for Infrared Small Target Detection

Infrared small target detection has significant applications in airspace surveillance, warning systems and so on. Detection of infrared small targets on a single image frame is a challenging work. First of all, compared to generic object detection, infrared small targets have the characteristics of low signal-to-clutter ratio, various sizes, and shapes. In addition, false alarm sources in the complex environment also impede precise detection. In the past years, scholars have performed many studies on infrared small target detection. The majority of traditional single-frame-based methods rely on the gray-scale discontinuity between the target and the neighborhood to detect infrared small targets. However, these traditional methods rely on prior assumptions. And when the real scenarios change dramatically, it is difficult to use fixed hyperparameters to handle such variations. As we all know, the deep convolutional neural network (CNN) can automatically learn the hierarchical features of images. However, CNN-based methods could not be directly applied for infrared small targets which are even small to several pixels because the pooling layer in the networks may result in the loss of targets.