Search Results for author:
Found 40 papers, 18 papers with code
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
In this context, we introduce a global-scale, high-quality image-text dataset for remote sensing, providing natural language descriptions for Sentinel-2 data to facilitate the understanding of satellite imagery for common users.
A Deep Active Contour Model for Delineating Glacier Calving Fronts
Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
RRSIS: Referring Remote Sensing Image Segmentation
Localizing desired objects from remote sensing images is of great use in practical applications.
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training
Based on the adversarial branch, we introduce two regularizers to constrain the training process against language bias.
GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data
Towards a fair and comprehensive analysis of existing methods, the proposed benchmark consists of 1) a large-scale dataset including co-registered RGB and nDSM pairs and pixel-wise semantic labels; 2) a comprehensive evaluation and analysis of existing multi-modal fusion strategies for both convolutional and Transformer-based networks on remote sensing data.
Ranked #1 on
Semantic Segmentation
on GAMUS
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images
With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
Anomaly Detection in Aerial Videos with Transformers
In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos.
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Furthermore, the holistic features are refined by the multi-scale temporal relations in a novel fusion module for yielding more discriminative video representations.
Self-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
Ranked #3 on
Multi-Label Image Classification
on BigEarthNet
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification.
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work.
Change Detection Meets Visual Question Answering
In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images.
Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks
Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging owing to the complexity of SAR data.
SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
In our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, the proposed model is tested directly on our collected Multi-scene Aerial Image (MAI) dataset.
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Ranked #2 on
Cross-Modal Retrieval
on SoundingEarth
Segmentation of VHR EO Images using Unsupervised Learning
The proposed method exploits this property to sample smaller patches from the larger scene and uses deep clustering and contrastive learning to refine the weights of a lightweight deep model composed of a series of the convolution layers along with an embedded channel attention.
A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features.
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images
We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively.
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-consuming to produce.
CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data.
Deep Learning Meets SAR
Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data.
Instance segmentation of buildings using keypoints
The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images.
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2, 864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
+1
Relation Network for Multi-label Aerial Image Classification
Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module.
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs).
R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
In this paper, we propose a novel deep network, called rotatable region-based residual network (R$^3$-Net), to detect multi-oriented vehicles in aerial images and videos.
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network.
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
We propose to tackle this problem with a semantic boundary-aware multi-task learning network.
RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images
The former is a classification CNN architecture for feature extraction, which takes an input image and produces multi-level convolutional feature maps from shallow to deep; while in the later, to achieve accurate boundary inference and semantic segmentation, boundary-aware high resolution feature maps in shallower layers and high-level but low-resolution features are recursively embedded into the learning framework (from deep to shallow) to generate a fused feature representation that draws a holistic picture of not only high-level semantic information but also low-level fine-grained details.
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis.
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed problem, with a large source of uncertainty coming from the overall scale.
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery.
Deep learning in remote sensing: a review
In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.
