Search Results for author:
Found 16 papers, 8 papers with code
RRSIS: Referring Remote Sensing Image Segmentation
Localizing desired objects from remote sensing images is of great use in practical applications.
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.
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.
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory.
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.
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.
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
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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).
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.
