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Review
Computer Science, Information Systems
Xian Sun et al.
Summary: This paper introduces the importance of multimodal observation in remote sensing and highlights the differences between single- and multimodal RS imagery interpretation. A cascaded structure research survey on multimodal RS imagery interpretation is conducted based on these differences. Finally, potential future research directions are explored.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Editorial Material
Geochemistry & Geophysics
Claudio Persello et al.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2023)
Article
Geochemistry & Geophysics
Xin Wu et al.
Summary: This paper proposes a new framework for multimodal remote sensing data classification, using deep learning and a cross-channel reconstruction module to learn compact fusion representations of different data sources. Extensive experiments on two multimodal RS datasets demonstrate the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Review
Geography, Physical
Xian Sun et al.
Summary: With the rapid development of deep learning, many deep learning-based approaches have achieved great success in object detection tasks. However, existing datasets have limitations in terms of scale, category, and image. To address the needs of high-resolution remote sensing images, we propose a novel benchmark dataset called FAIR1M, which includes over 1 million instances and more than 40,000 images for fine-grained object recognition. The FAIR1M dataset has several unique characteristics, such as being larger than other datasets, providing richer category information, containing geographic information, and having better image quality. We evaluate state-of-the-art methods on the FAIR1M dataset and propose improvements to the evaluation metrics and the incorporation of hierarchy detection. We believe that the FAIR1M dataset will contribute to the field of earth observation through fine-grained object detection in large-scale real-world scenes.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geography, Physical
Yongqiang Mao et al.
Summary: In this article, a novel receptive field fusion-and stratification network (RFFS-Net) is proposed to address the challenging task of classifying large-scale airborne laser scanning (ALS) point clouds with complex structures and scale variations. By utilizing various receptive field features and multi-level decoders, the network achieves state-of-the-art classification performance on multiple datasets.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geography, Physical
Yansheng Li et al.
Summary: Land use and land cover maps are important for various studies, but existing classification methods have limitations. In this study, the authors propose a domain knowledge guided deep collaborative fusion network (DKDFN) for land cover classification. DKDFN combines multiple modalities and incorporates domain knowledge effectively. The network is trained using an asymmetry loss function (ALF) to improve performance, especially on low frequency categories.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Xiu-Shen Wei et al.
Summary: Fine-grained image analysis is a longstanding and fundamental problem in computer vision, and has seen remarkable progress driven by deep learning. This field involves analyzing visual objects from subordinate categories and presents challenges due to small inter-class and large intra-class variation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Review
Engineering, Electrical & Electronic
ZhiYong Lv et al.
Summary: This article provides an overview of change detection with heterogeneous remote sensing images (Hete-CD), reviews major techniques, compares classical methods, and concludes challenges, opportunities, and future directions for Hete-CD.
PROCEEDINGS OF THE IEEE
(2022)
Article
Computer Science, Artificial Intelligence
Jian Ding et al.
Summary: This paper presents a large-scale DOTA dataset for object detection in aerial images, along with comprehensive baselines and a code library. The dataset and evaluations provided can facilitate the design of robust algorithms and reproducible research in the field of object detection in aerial images.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Geography, Physical
Yu Tian et al.
Summary: In this paper, the authors address the challenge of interpreting large-scale aerial imagery and propose a lightweight hypergraph construction strategy to improve the modeling of non-local relations. They apply this strategy in the spatial dimension and construct a fully-weighted hypergraph neural network to capture short- and long-range dependencies. They further design a hypergraph convolutional feature pyramid network to learn non-local relations at different scales and aggregate hierarchical global contexts. Experimental results demonstrate that their method significantly improves the performance in geospatial visual recognition and can be easily integrated with state-of-the-art architectures.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Steve Frolking et al.
Summary: Most urban information maps are two-dimensional, lacking knowledge about global patterns and changes in urban built-up volumes and development intensity. This study uses a global microwave backscatter dataset to explore the third dimension of urban growth across 477 large cities. The results show a correlation between urban backscatter and building volume, with varying rates of increase across different regions and decades.
REMOTE SENSING OF ENVIRONMENT
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Xingliang Huang et al.
Summary: The dataset focuses on individual building-level interpretation from high-resolution satellite imagery, providing in-depth descriptions of building geometry and functionality. It serves as a flexible test platform for algorithms and a solid foundation for comparing city morphologies and urban planning research.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2022
(2022)
Article
Geochemistry & Geophysics
Zhiyong Lv et al.
Summary: Land cover change detection using remote sensing images is important for various applications. This article proposes a novel neural network method with spatial-spectral attention mechanism and multiscale dilation convolution modules to enhance the detection accuracy. The proposed method achieves superior performance compared to state-of-the-art methods in terms of quantitative evaluation metrics and visual performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhiyong Lv et al.
Summary: This letter proposes a simple yet effective deep learning approach based on the classical UNet for change detection with heterogeneous remote sensing images (HRSIs). The approach utilizes image patch concatenation, multiscale convolution module, and a combined loss function to improve the detection performance. Experimental results demonstrate the feasibility and superiority of the proposed approach in detecting land cover change with HRSIs.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
David Frantz et al.
Summary: This study mapped building heights for entire Germany using Sentinel-1A/B and Sentinel-2A/B time series, achieving good results in training machine learning models. The synergistic combination of radar and optical models led to superior prediction results, and there were significant differences in average building heights across different regions in Germany.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Fang Fang et al.
Summary: This study proposes a coarse-to-fine contour optimization network for improving the performance of building instance extraction from high-resolution remote sensing imagery. The network consists of two special sub-networks, AFPN and coarse-to-fine contour sub-network, to accurately extract building contours. Experimental results show that the proposed method outperformed state-of-the-art methods in terms of accuracy and quality of building contours.
Article
Geochemistry & Geophysics
Danfeng Hong et al.
Summary: This study introduces a general multimodal deep learning (MDL) framework for the classification and identification challenges in geoscience and remote sensing. By investigating a special case of multi-modality learning (MML), the study presents five fusion strategies and demonstrates how to train deep networks and build network architectures effectively. Experimental results on two different multimodal RS data sets confirm the efficiency and advantages of the proposed MDL framework.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geography, Physical
Danfeng Hong et al.
Summary: With the availability of remote sensing data from different sensors, there is growing interest in multimodal data processing and analysis techniques. A shared and specific feature learning (S2FL) model is proposed to blend multimodal information effectively for land cover classification. Extensive experiments on three benchmark datasets demonstrate the superiority and advancements of the S2FL model in comparison to state-of-the-art baselines.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Junshi Xia et al.
Summary: Deep learning is popular in remote sensing, but there is still untapped potential for synthetic aperture radar (SAR) data, especially very high resolution (VHR) SAR. This article provides a benchmark dataset and reviews state-of-the-art methods to improve SAR semantic segmentation in the future.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Fabien H. Wagner et al.
Article
Environmental Sciences
Xin Huang et al.
REMOTE SENSING OF ENVIRONMENT
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Qingyu Li et al.
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2020)
Article
Computer Science, Artificial Intelligence
Sultan Daud Khan et al.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2019)
Article
Computer Science, Artificial Intelligence
Tsung-Yu Lin et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Leonid Karlinsky et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaiming He et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Ross Girshick
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2015)
