Search Results for author: Lichao Mou

Found 40 papers, 18 papers with code

ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models

no code implementations17 Feb 2024 Zhenghang Yuan, Zhitong Xiong, Lichao Mou, Xiao Xiang Zhu

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.

Earth Observation Semantic Segmentation

A Deep Active Contour Model for Delineating Glacier Calving Fronts

no code implementations7 Jul 2023 Konrad Heidler, Lichao Mou, Erik Loebel, Mirko Scheinert, Sébastien Lefèvre, Xiao Xiang Zhu

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.

Contour Detection Edge Detection +2

RRSIS: Referring Remote Sensing Image Segmentation

no code implementations14 Jun 2023 Zhenghang Yuan, Lichao Mou, Yuansheng Hua, Xiao Xiang Zhu

Localizing desired objects from remote sensing images is of great use in practical applications.

Benchmarking Image Segmentation +2

GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data

1 code implementation24 May 2023 Zhitong Xiong, Sining Chen, Yi Wang, Lichao Mou, Xiao Xiang Zhu

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.

Segmentation Semantic Segmentation

Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images

no code implementations7 Apr 2023 Zhenghang Yuan, Lichao Mou, Xiao Xiang Zhu

With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning.

Contrastive Learning Question Answering +1

Anomaly Detection in Aerial Videos with Transformers

1 code implementation25 Sep 2022 Pu Jin, Lichao Mou, Gui-Song Xia, Xiao Xiang Zhu

In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos.

Anomaly Detection

FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification

no code implementations22 Sep 2022 Pu Jin, Lichao Mou, Yuansheng Hua, Gui-Song Xia, Xiao Xiang Zhu

Furthermore, the holistic features are refined by the multi-scale temporal relations in a novel fusion module for yielding more discriminative video representations.

Action Recognition Temporal Action Localization +1

Self-supervised Learning in Remote Sensing: A Review

2 code implementations27 Jun 2022 Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu

In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.

Earth Observation Multi-Label Image Classification +1

Change Detection Meets Visual Question Answering

1 code implementation12 Dec 2021 Zhenghang Yuan, Lichao Mou, Zhitong Xiong, Xiaoxiang Zhu

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.

Answer Generation Change Detection +3

Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks

no code implementations18 Nov 2021 Yao Sun, Lichao Mou, Yuanyuan Wang, Sina Montazeri, Xiao Xiang Zhu

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.

regression Retrieval

SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images

1 code implementation15 Aug 2021 Tianze Yu, Jianzhe Lin, Lichao Mou, Yuansheng Hua, Xiaoxiang Zhu, Z. Jane Wang

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.

Multi-Label Image Classification Multi-Label Learning +1

Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

1 code implementation13 Aug 2021 Lei Ding, Haitao Guo, Sicong Liu, Lichao Mou, Jing Zhang, Lorenzo Bruzzone

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.

Change Detection

Self-supervised Audiovisual Representation Learning for Remote Sensing Data

1 code implementation2 Aug 2021 Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu

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.

Cross-Modal Retrieval Representation Learning +1

Segmentation of VHR EO Images using Unsupervised Learning

no code implementations9 Jul 2021 Sudipan Saha, Lichao Mou, Muhammad Shahzad, Xiao Xiang Zhu

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.

Contrastive Learning Deep Clustering +3

MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images

1 code implementation7 Apr 2021 Yuansheng Hua, Lichao Mou, Pu Jin, Xiao Xiang Zhu

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.

Learning with noisy labels Scene Recognition

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

1 code implementation15 Mar 2021 Lichao Mou, Sudipan Saha, Yuansheng Hua, Francesca Bovolo, Lorenzo Bruzzone, Xiao Xiang Zhu

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.

Classification General Classification +4

Semantic Segmentation of Remote Sensing Images with Sparse Annotations

1 code implementation10 Jan 2021 Yuansheng Hua, Diego Marcos, Lichao Mou, Xiao Xiang Zhu, Devis Tuia

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.

Semantic Segmentation

CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images

no code implementations17 Nov 2020 Yao Sun, Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu

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.

Retrieval Segmentation

Deep Learning Meets SAR

no code implementations17 Jun 2020 Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler

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

no code implementations6 Jun 2020 Qingyu Li, Lichao Mou, Yuansheng Hua, Yao Sun, Pu Jin, Yilei Shi, Xiao Xiang Zhu

The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.

Instance Segmentation Segmentation +1

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

1 code implementation ECCV 2020 Di Hu, Xuhong LI, Lichao Mou, Pu Jin, Dong Chen, Liping Jing, Xiaoxiang Zhu, Dejing Dou

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.

Scene Recognition

Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

1 code implementation14 May 2020 Di Hu, Lichao Mou, Qingzhong Wang, Junyu. Gao, Yuansheng Hua, Dejing Dou, Xiao Xiang Zhu

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images.

Crowd Counting

ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos

no code implementations30 Jan 2020 Lichao Mou, Yuansheng Hua, Pu Jin, Xiao Xiang Zhu

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

1 code implementation19 Dec 2019 Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Häberle, Yuansheng Hua, Rong Huang, Lloyd Hughes, Hao Li, Yao Sun, Guichen Zhang, Shiyao Han, Michael Schmitt, Yuanyuan Wang

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

1 code implementation16 Jul 2019 Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu

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.

Classification General Classification +5

R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

no code implementations16 Aug 2018 Qingpeng Li, Lichao Mou, Qizhi Xu, Yun Zhang, Xiao Xiang Zhu

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.

Motion Estimation Position +1

Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification

no code implementations30 Jul 2018 Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu

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.

General Classification Image Classification +1

RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images

no code implementations5 May 2018 Lichao Mou, Xiao Xiang Zhu

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.

Segmentation Semantic Segmentation

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

no code implementations7 Mar 2018 Lichao Mou, Lorenzo Bruzzone, Xiao Xiang Zhu

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.

Change Detection Earth Observation

IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network

1 code implementation28 Feb 2018 Lichao Mou, Xiao Xiang Zhu

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.

Instance Segmentation Semantic Segmentation

Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

no code implementations25 Jan 2018 Lloyd H. Hughes, Michael Schmitt, Lichao Mou, Yuanyuan Wang, Xiao Xiang Zhu

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.

Key Point Matching

Deep learning in remote sensing: a review

1 code implementation11 Oct 2017 Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer

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.

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