Earth observations from remotesensingimagery play an important role in many environmental applications ranging from natural resource (e.g., crops, forests) monitoring to man-made object (e.g., builds, factories) rec...
详细信息
ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Earth observations from remotesensingimagery play an important role in many environmental applications ranging from natural resource (e.g., crops, forests) monitoring to man-made object (e.g., builds, factories) recognition. Most widely used optical remotesensing data however is often contaminated by clouds making it hard to identify the objects underneath. Fortunately, with the recent advances and increased operational satellites, the spatial and temporal density of image collections have significantly increased. In this paper, we present a novel deep learning-based imputation technique for inferring spectral values under the clouds using nearby cloud-free satellite image observations. The proposed deep learning architecture, extended contextual attention (ECA), exploits similar properties from the cloud-free areas to tackle clouds of different sizes occurring at arbitrary locations in the image. A contextual attention mechanism is incorporated to utilize the useful cloud-free information from multiple images. To maximize the imputation performance of the model on the cloudy patches instead of the entire image, a two-phase custom loss function is deployed to guide the model. To study the performance of our model, we trained our model on a benchmark Sentinel-2 dataset by superimposing real-world cloud patterns. Extensive experiments and comparisons against the state-of-the-art methods using pixel-wise and structural metrics show the improved performance of our model. Our experiments demonstrated that the ECA method is consistently better than all other methods, it is 28.4% better on MSE and 31.7% better on cloudy MSE as compared to the state-of-the-art EDSR network.
The proceedings contain 54 papers. The topics discussed include: graph feature embeddings for patient re-identification from chest X-ray images;zero-shot synth-to-real depth estimation: from synthetic street scenes to...
ISBN:
(纸本)9798350376036
The proceedings contain 54 papers. The topics discussed include: graph feature embeddings for patient re-identification from chest X-ray images;zero-shot synth-to-real depth estimation: from synthetic street scenes to real-world data;improving FLIM-based salient object detection networks with cellular automata;advanced virtual human modeling with metahumans: focus on genderless characters;a database for soybean seed classification;complex-valued embedding on Gassmann manifolds for pattern set representation;learning visual patterns in remotesensing: an overview of agricultural applications;and spherically-weighted horizontally dilated convolutions for omnidirectional imageprocessing.
The proceedings contain 16 papers. The topics discussed include: fully electrically controlled light-field camera via electrowetting liquid lens and liquid-crystal microlens array;study on transmission and nanofocusin...
ISBN:
(纸本)9781510674912
The proceedings contain 16 papers. The topics discussed include: fully electrically controlled light-field camera via electrowetting liquid lens and liquid-crystal microlens array;study on transmission and nanofocusing characteristics of surface array micronano metasurface;tuning of near-field optical properties based on magneto-tip array super-surfaces;ice area and 3D ice shape measurement method based on polarized light imaging;study on the polarization response of aluminum gratings with graphene;toroidal composite liquid crystal microlens array co-driven by four independent signal voltages;semi-supervised polarimetric SAR images classification based on FixMatch;and overview of remotesensingimage fusion based on deep learning.
This article discusses the servo control technology for the automatic screw-tightening process of a robotic arm based on multiple visual sensors, aiming at the assembly requirements of complex spatial structural compo...
详细信息
Despite the remarkable progress has made in deep compressed sensing (DCS), how to improve the reconstruction quality is still a major challenge. The existing DCS model generally still has some issues, especially in re...
详细信息
The proceedings contain 49 papers. The special focus in this conference is on patternrecognition. The topics include: Virtualization and 3D Visualization of Historical Costume Replicas: Accessibility, Inclu...
ISBN:
(纸本)9783031377440
The proceedings contain 49 papers. The special focus in this conference is on patternrecognition. The topics include: Virtualization and 3D Visualization of Historical Costume Replicas: Accessibility, Inclusivity, Virtuality;datafication of an Ancient Greek City: Multi-sensorial remotesensing of Heloros (Sicily);geolocation of Cultural Heritage Using Multi-view Knowledge Graph Embedding;masonry Structure Analysis, Completion and Style Transfer Using a Deep Neural Network;PARTICUL: Part Identification with Confidence Measure Using Unsupervised Learning;Explaining Classifications to Non-experts: An XAI User Study of Post-Hoc Explanations for a Classifier When People Lack Expertise;motif-Guided Time Series Counterfactual Explanations;comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study;graph-Based Analysis of Hierarchical Embedding Generated by Deep Neural Network;Explainability of image Semantic Segmentation Through SHAP Values;CIELab Color Measurement Through RGB-D images;comparing Feature Importance and Rule Extraction for Interpretability on Text Data;physics-Informed Neural Networks for Solar Wind Prediction;less Labels, More Modalities: A Self-Training Framework to Reuse Pretrained Networks;feature Transformation for Cross-domain Few-Shot remotesensing Scene Classification;a Novel Methodology for High Resolution Sea Ice Motion Estimation;Deep Learning-Based Sea Ice Lead Detection from WorldView and Sentinel SAR imagery;Uncertainty Analysis of Sea Ice and Open Water Classification on SAR imagery Using a Bayesian CNN;a Two-Stage Road Segmentation Approach for remotesensingimages;YUTO Tree5000: A Large-Scale Airborne LiDAR Dataset for Single Tree Detection;spatial Layout Consistency for 3D Semantic Segmentation;reconstruction of Cultural Heritage 3D Models from Sparse Point Clouds Using Implicit Neural Representations;From TrashCan to UNO: Deriving an Underwater image Dataset to Get a More Consistent and
Modern neural networks achieve state-of-the-art results on land cover classification from satellite imagery, as is the case for almost all vision tasks. One of the main challenges in this context is dealing with geogr...
详细信息
ISBN:
(纸本)9783031546044;9783031546051
Modern neural networks achieve state-of-the-art results on land cover classification from satellite imagery, as is the case for almost all vision tasks. One of the main challenges in this context is dealing with geographic variability in both image and label distributions. To tackle this problem, we study the effectiveness of incorporating bioclimatic information into neural network training and prediction. Such auxiliary data can easily be extracted from freely available rasters at satellite images' georeferenced locations. We compare two methods of incorporation, learned embeddings and conditional batch normalization, to a bioclimate-agnostic baseline ResNet18. In our experiments on the EuroSAT and BigEarthNet datasets, we find that especially the use of conditional batch normalization improves the network's overall accuracy, generalizability, as well as training efficiency, in both a supervised and a self-supervised learning setup. Code and data are publicly available at https://***/NDQFF.
The proceedings contain 49 papers. The special focus in this conference is on patternrecognition. The topics include: Virtualization and 3D Visualization of Historical Costume Replicas: Accessibility, Inclu...
ISBN:
(纸本)9783031377303
The proceedings contain 49 papers. The special focus in this conference is on patternrecognition. The topics include: Virtualization and 3D Visualization of Historical Costume Replicas: Accessibility, Inclusivity, Virtuality;datafication of an Ancient Greek City: Multi-sensorial remotesensing of Heloros (Sicily);geolocation of Cultural Heritage Using Multi-view Knowledge Graph Embedding;masonry Structure Analysis, Completion and Style Transfer Using a Deep Neural Network;PARTICUL: Part Identification with Confidence Measure Using Unsupervised Learning;Explaining Classifications to Non-experts: An XAI User Study of Post-Hoc Explanations for a Classifier When People Lack Expertise;motif-Guided Time Series Counterfactual Explanations;comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study;graph-Based Analysis of Hierarchical Embedding Generated by Deep Neural Network;Explainability of image Semantic Segmentation Through SHAP Values;CIELab Color Measurement Through RGB-D images;comparing Feature Importance and Rule Extraction for Interpretability on Text Data;physics-Informed Neural Networks for Solar Wind Prediction;less Labels, More Modalities: A Self-Training Framework to Reuse Pretrained Networks;feature Transformation for Cross-domain Few-Shot remotesensing Scene Classification;a Novel Methodology for High Resolution Sea Ice Motion Estimation;Deep Learning-Based Sea Ice Lead Detection from WorldView and Sentinel SAR imagery;Uncertainty Analysis of Sea Ice and Open Water Classification on SAR imagery Using a Bayesian CNN;a Two-Stage Road Segmentation Approach for remotesensingimages;YUTO Tree5000: A Large-Scale Airborne LiDAR Dataset for Single Tree Detection;spatial Layout Consistency for 3D Semantic Segmentation;reconstruction of Cultural Heritage 3D Models from Sparse Point Clouds Using Implicit Neural Representations;From TrashCan to UNO: Deriving an Underwater image Dataset to Get a More Consistent and
Effective differentiation of aircraft types using visible remotesensingimages is important for providing military combat information as well as civilian aircraft operations. With the emergence of deep learning, remo...
详细信息
At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remotesensing ...
详细信息
暂无评论