Article
Geochemistry & Geophysics
Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot
Summary: The article introduces a novel HS image classification network called SpectralFormer, which utilizes the transformers framework to learn spectral sequence information, achieving better classification performance than traditional methods and exhibiting high flexibility.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Hao Chen, Zipeng Qi, Zhenwei Shi
Summary: This study introduces a bitemporal image transformer (BIT) for efficient and effective change detection by modeling contexts in the spatial-temporal domain. The BIT model demonstrates superior performance and efficiency on three CD datasets, significantly outperforming the purely convolutional baseline model with lower computational costs.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jiaming Han, Jian Ding, Jie Li, Gui-Song Xia
Summary: Significant progress has been made in the past decade on detecting objects in aerial images. We propose a single-shot alignment network (S(2)A-Net) that consists of two modules to address the misalignment issue between anchors and convolutional features, improving the consistency between classification score and localization accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Sheng Fang, Kaiyu Li, Jinyuan Shao, Zhe Li
Summary: This letter proposes a densely connected siamese network (SNUNet-CD) for change detection, which alleviates the loss of localization information in deep layers and introduces ECAM for deep supervision. Experimental results show that the method achieves a better tradeoff between accuracy and calculation amount compared to other state-of-the-art change detection methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Le Sun, Guangrui Zhao, Yuhui Zheng, Zebin Wu
Summary: In this article, the spectral-spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral-spatial and high-level semantic features. Experimental analysis confirms that this method outperforms other deep learning methods in terms of computation time and classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xintong Dong, Yue Li, Tie Zhong, Ning Wu, Hongzhou Wang
Summary: Distributed fiber-optical acoustic sensing (DAS) is a promising technology in seismic exploration, but the quality of DAS-VSP data is often affected by random and coherent noises. To improve the signal-to-noise ratio, a CNN model based on L-FM-CNN is proposed, utilizing leaky ReLU as the activation function. By constructing a high-authenticity theoretical seismic data set and using a new loss function ERM, the proposed method proves effective in denoising DAS-VSP data with different SNRs.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Weiwei Cai, Zhanguo Wei
Summary: In this letter, a novel cross-attention mechanism and graph convolution integration algorithm are proposed for hyperspectral data classification. Experimental results show that the proposed algorithm achieves better performances than other algorithms using different methods of training set division.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Xin Wu, Danfeng Hong, Jocelyn Chanussot
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)
Article
Geochemistry & Geophysics
Qian Shi, Mengxi Liu, Shengchen Li, Xiaoping Liu, Fei Wang, Liangpei Zhang
Summary: The article proposes a deeply supervised attention metric-based network (DSAMNet) to address the challenges in change detection. The network uses a metric module for deep metric learning, integrated with convolutional block attention modules (CBAM), and a DS module to enhance feature extraction and generate more useful features. A new CD dataset, Sun Yat-Sen University (SYSU)-CD, containing 20,000 aerial image pairs, is also created for bitemporal image CD. Experimental results show that the network achieves the highest accuracy on both datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Fulin Luo, Zehua Zou, Jiamin Liu, Zhiping Lin
Summary: The research proposes a multistructure unified discriminative embedding (MUDE) method to extract the low-dimensional features of hyperspectral image (HSI), by considering the neighborhood, tangential, and statistical properties of each sample in HSI for achieving the complementarity of different characteristics. Experimental results demonstrate that the proposed method can improve the classification performance of HSI.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Jing Wei, Zhanqing Li, Ke Li, Russell R. Dickerson, Rachel T. Pinker, Jun Wang, Xiong Liu, Lin Sun, Wenhao Xue, Maureen Cribb
Summary: This study presents a new method to estimate ground-level ozone concentrations in China using solar radiation intensity and surface temperature. The generated dataset provides reliable and valuable information, including short-term severe ozone pollution events and the rapid increase and recovery of ozone concentrations associated with changes in anthropogenic emissions. The study also reveals an increase in summertime ozone concentrations and the probability of daily ozone pollution since 2015, with a decline observed in 2020 due to air pollution control measures and the COVID-19 pandemic.
REMOTE SENSING OF ENVIRONMENT
(2022)
Review
Geography, Physical
Xian Sun, Peijin Wang, Zhiyuan Yan, Feng Xu, Ruiping Wang, Wenhui Diao, Jin Chen, Jihao Li, Yingchao Feng, Tao Xu, Martin Weinmann, Stefan Hinz, Cheng Wang, Kun Fu
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
Geochemistry & Geophysics
Rui Li, Shunyi Zheng, Ce Zhang, Chenxi Duan, Jianlin Su, Libo Wang, Peter M. Atkinson
Summary: Semantic segmentation of remote sensing images is vital for various applications such as land resource management and urban planning. Despite the improvement in accuracy with deep convolutional neural networks, standard models have limitations like underuse of information and insufficient exploration of long-range dependencies. This article introduces a multiattention network (MANet) with efficient attention modules to address these issues and demonstrates superior performance on large-scale remote sensing datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yao Ding, Xiaofeng Zhao, Zhili Zhang, Wei Cai, Nengjun Yang, Ying Zhan
Summary: A novel dense graph neural network structure incorporating ARMA filters, dense structure, and context-aware learning mechanism has been proposed and applied successfully to hyperspectral image classification. Experimental results demonstrated its superiority over current state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Uzair Aslam Bhatti, Zhaoyuan Yu, Jocelyn Chanussot, Zeeshan Zeeshan, Linwang Yuan, Wen Luo, Saqib Ali Nawaz, Mughair Aslam Bhatti, Qurat ul Ain, Anum Mehmood
Summary: This article introduces a novel HSI classification algorithm, LSPGF, which utilizes LSP and Gabor filtering to extract deeper features from original hyperspectral data, achieving higher classification accuracy compared to other algorithms.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Lefei Zhang, Liangpei Zhang
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
Review
Remote Sensing
Syed Agha Hassnain Mohsan, Muhammad Asghar Khan, Fazal Noor, Insaf Ullah, Mohammed H. Alsharif
Summary: This article discusses the importance and potential of drones in various application fields, as well as the challenges and security issues they face. It also presents future research directions and areas that need to be addressed.
Article
Geochemistry & Geophysics
Xin He, Yong Zhou, Jiaqi Zhao, Di Zhang, Rui Yao, Yong Xue
Summary: In this paper, a novel semantic segmentation framework called ST-UNet is proposed for remote sensing images. By incorporating Swin Transformer and CNN, the framework achieves improved segmentation accuracy. The introduced spatial interaction module, feature compression module, and relational aggregation module effectively utilize global context information and local features, leading to significant performance improvements.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yi Li, Lan Du, Di Wei
Summary: This article presents a multiscale convolutional neural network (CNN) based on component analysis (CA-MCNN) for synthetic aperture radar (SAR) automatic target recognition (ATR). By combining component information with global information, CA-MCNN achieves a more efficient and robust representation of target features. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the superior performance of CA-MCNN.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Wei Liu, Karoll Quijano, Melba M. Crawford
Summary: This paper proposes a tassel detection algorithm based on UAV imagery, which achieves better performance in small-size tassel detection. The algorithm adopts novel techniques such as the bidirectional feature pyramid network and the robust attention module.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
