Article
Environmental Sciences
Yibo Wang, Xia Zhang, Changping Huang, Wenchao Qi, Jinnian Wang, Xiankun Yang, Songtao Ding, Shiyu Tao
Summary: A Spatial-Convolution Spectral-Transformer Interactive Network (SCSTIN) model was proposed to enhance feature extraction capabilities and address the challenges in satellite hyperspectral imagery classification and mapping. The model achieved satisfactory performance in accuracy and efficiency, making it reliable for large-scale fast refined land cover classification and mapping.
Article
Geochemistry & Geophysics
Zhe Gao, Bin Pan, Xia Xu, Tao Li, Zhenwei Shi
Summary: This article introduces a domain generalization technique to address the problem of hyperspectral heterospectra in pixel-wise classification. The proposed LiCa block aligns the spectral conditional distributions of different domains to achieve better generalization performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Huan Liu, Wei Li, Xiang-Gen Xia, Mengmeng Zhang, Chen-Zhong Gao, Ran Tao
Summary: This article addresses the issue of spectral shift in cross-scene hyperspectral imagery classification by proposing spectral shift mitigation (SSM) that includes amplitude shift mitigation (ASM) and adjacency effect mitigation (AEM). By reducing amplitude shift and spectral variation through amplitude normalization and weighted average spectral vector methods, and using a classifier trained with labeled samples from the source scene, superior classification performance is achieved on several cross-scene HSI data pairs.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Bo Zhao, Peng Gou, Fan Yang, Panpan Tang
Summary: This study proposes a novel algorithm for image classification that explores statistical distinctions and refines or updates existing land-cover classification results by comparing the similarity between different image segments. The algorithm, based on object-oriented image analysis, demonstrates good performance when evaluated on existing GIS base maps.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Bobo Xie, Yifan Zhang, Shaohui Mei, Ge Zhang, Yan Feng, Qian Du
Summary: In this article, a spectral variation augmented representation for hyperspectral imagery classification with few labeled samples is proposed, which significantly outperforms compared methods in cases with limited labeled samples. The method includes a novel linear representation model and spectral variation extraction schemes to enhance classification accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
K. R. Sivabalan, E. Ramaraj
Summary: Remote sensing imagery classification plays a role in providing assistance for comfort and societal security. The use of multispectral high-resolution imagery provides detailed information about the Earth's surface, while phenology reflection varies based on land cover type.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Review
Environmental Sciences
Mohammed Abdulmajeed Moharram, Divya Meena Sundaram
Summary: This paper provides an extensive and systematic survey of hyperspectral dimensionality reduction approaches for land use land cover (LULC) classification. It covers methods of acquiring hyperspectral imaging data, the difference between hyperspectral and multispectral images, dimensionality reduction based on machine learning and deep learning techniques, popular benchmark datasets for LULC classification, and significant challenges and future trends.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Agronomy
Rongchao Yang, Qingbo Zhou, Beilei Fan, Yuting Wang, Zhemin Li
Summary: The continuous changes in Land Use and Land Cover (LULC) have significant impact on the environment, therefore accurate monitoring and updating of land cover information are crucial for environmental protection, sustainable development, and land resource planning and management. This paper proposes a novel Weighted Spatial-Spectral Joint CR Classification (WSSJCRC) method, which incorporates spatial filtering and spatial structure information to improve land cover classification performance.
Article
Environmental Studies
Rongchao Yang, Beilei Fan, Ren Wei, Yuting Wang, Qingbo Zhou
Summary: This paper proposes a land cover classification method based on kernel collaborative representation method and introduces a correlation coefficient-weighted spatial filtering operation to reduce spectral shift. Through experimental comparison, the proposed weighted spatial-spectral KCRT (WSSKCRT) method achieves the best classification performance in the case of small-size labeled samples.
Article
Remote Sensing
Xiong Tan, Zhixiang Xue
Summary: This paper proposes a novel spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. The network utilizes multi-layer perceptron to represent and classify hyperspectral images, which improves the classification performance compared to current deep learning methods. Experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy.
EUROPEAN JOURNAL OF REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Bobo Xi, Jiaojiao Li, Yan Diao, Yunsong Li, Zan Li, Yan Huang, Jocelyn Chanussot
Summary: This paper proposes a novel deep generative spectral-spatial classifier (DGSSC) to address the issues of imbalanced hyperspectral image classification. The DGSSC consists of two encoders, a decoder, and a classifier, which are trained in an end-to-end manner. The encoder utilizes 3D and 2D convolutions to explore spectral-spatial and deep spatial information, while the second stage employs a deep latent variable model for minority-class data augmentation. Experimental results on three benchmark datasets demonstrate the superiority of the proposed DGSSC method in terms of prediction robustness, outperforming state-of-the-art methods. For instance, DGSSC achieves a mean overall accuracy of 97.85% with a standard deviation of 0.24% on the University of Pavia dataset using 1% randomly selected imbalanced training samples.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Bahadir Celik
Summary: Environmental monitoring studies rely on accurate and up-to-date land use and land cover information, and remote sensing data/techniques are widely used for land cover mapping due to their synoptic view and high temporal resolution capabilities. Linear spectral unmixing technique provides sub-pixel level land cover information, unlike traditional image classification. In this paper, the QLSU plugin, an open source and user-friendly graphical interface tool implemented in QGIS, is introduced for researchers without programming experience to perform linear spectral unmixing on remote sensing imagery. A case study on both real and synthetic images is conducted to demonstrate the plugin's usage and evaluate its results.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Engineering, Electrical & Electronic
Guichen Zhang, Paul Scheunders, Daniele Cerra, Rupert Mueller
Summary: In this article, a physics-based spectral mixture model called the extended shadow multilinear mixing (ESMLM) model is proposed, which can adapt to different ground surface scenarios with varying illumination conditions and shadows. The model performs robustly and provides physically interpretable parameters that contain valuable information on the scene structures.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Fei Tong, Yun Zhang
Summary: In recent years, deep learning methods have been widely used in hyperspectral image (HSI) classification. A new classification method called spectral-spatial deep RF (SSDRF) is proposed to fully utilize the spatial information in HSIs for improved classification accuracy, combining fixed-size patches with shape-adaptive superpixels to exploit more accurate spatial information. This approach outperforms patched-based DCDRF and achieves satisfactory classification results.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Shanshan Feng, Fenglei Fan
Summary: This study analyzed the effect of spectral interference of mixed pixels and identified the thresholds causing such interference using spectral similarity measures. The thresholds of abundance causing mixed-pixel spectral interference in vegetation, high-albedo impervious surface, low-albedo impervious surface, and soil endmembers were determined to be 70%, 75%, 80%, and 70% respectively. Therefore, when the endmember abundance within mixed pixels exceeds these thresholds, the mixed spectra are interfered and exhibit as a pure spectral characteristic.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)
