本研究探索合成孔径雷达（Synthetic Aperture Radar, SAR）影像与可见光-近红外-短波红外（Visible-Near Infrared-Short Wave Infrared, VNIR-SWIR）影像的协同组合，用于土地利用/土地覆盖（land use/land cover, LULC）分类。研究采用贝叶斯融合（Bayesian fusion）的图像融合方法，将SAR纹理波段与VNIR-SWIR影像进行融合，旨在探究这种融合方式对LULC分类的影响。尽管随机森林在监督分类中应用广泛，但其存在特征较少时性能欠佳、精度停滞等局限性，本研究针对这些问题展开。为克服上述问题，研究构建了随机森林集成（ensembles of random forests, RFE），并通过Forest-RC算法引入随机旋转机制，采用了三种旋转方法：主成分分析（principal component analysis, PCA）、稀疏随机旋转（sparse random rotation, SRP）矩阵和完全随机旋转（complete random rotation, CRP）矩阵。训练数据集来自印度理工学院坎普尔（IIT-Kanpur）地区的Sentinel-1 SAR数据与Sentinel-2 VNIR-SWIR数据，包含SAR、带纹理的SAR、VNIR-SWIR、带纹理的VNIR-SWIR以及带纹理的融合VNIR-SWIR共五类数据集。研究评估了分类器的效能，探究了SAR与VNIR-SWIR影像融合对分类的影响，还显著提升了贝叶斯融合代码的执行速度。实验结果显示，基于SRP的RFE在前两个数据集上表现优于其他集成方法，平均总体kappa值分别为61.80%和68.18%；基于CRP的RFE在后三个数据集上表现最优，平均总体kappa值分别为95.99%、96.93%和96.30%，其中第四个数据集取得了最高的96.93%总体kappa值。此外，在SAR波段中加入纹理特征可使总体kappa值最高提升10.00%，而在VNIR-SWIR波段中加入纹理特征最高可带来约3.45%的提升。
英文摘要：
The study explores the synergistic combination of Synthetic Aperture Radar (SAR) and Visible-Near Infrared-Short Wave Infrared (VNIR-SWIR) imageries for land use/land cover (LULC) classification. Image fusion, employing Bayesian fusion, merges SAR texture bands with VNIR-SWIR imageries. The research aims to investigate the impact of this fusion on LULC classification. Despite the popularity of random forests for supervised classification, their limitations, such as suboptimal performance with fewer features and accuracy stagnation, are addressed. To overcome these issues, ensembles of random forests (RFE) are created, introducing random rotations using the Forest-RC algorithm. Three rotation approaches: principal component analysis (PCA), sparse random rotation (SRP) matrix, and complete random rotation (CRP) matrix are employed. Sentinel-1 SAR data and Sentinel-2 VNIR-SWIR data from the IIT-Kanpur region constitute the training datasets, including SAR, SAR with texture, VNIR-SWIR, VNIR-SWIR with texture, and fused VNIR-SWIR with texture. The study evaluates classifier efficacy, explores the impact of SAR and VNIR-SWIR fusion on classification, and significantly enhances the execution speed of Bayesian fusion code. The SRP-based RFE outperforms other ensembles for the first two datasets, yielding average overall kappa values of 61.80% and 68.18%, while the CRP-based RFE excels for the last three datasets with average overall kappa values of 95.99%, 96.93%, and 96.30%. The fourth dataset achieves the highest overall kappa of 96.93%. Furthermore, incorporating texture with SAR bands results in a maximum overall kappa increment of 10.00%, while adding texture to VNIR-SWIR bands yields a maximum increment of approximately 3.45%.
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Asian Development Bank, Philippines（亚洲开发银行，菲律宾）；GeoVille Information Systems and Data Processing GmbH, A-6020 Innsbruck, Austria（GeoVille信息系统和数据处理公司，奥地利）；ETH Zürich, Switzerland（苏黎世联邦理工学院，瑞士）
ETH Zurich（苏黎世联邦理工学院）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）
The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40->20 m, respectively 360->60 m GSD. In this way, one has access to a virtually infinite amount of tr…
IRSTEA（法国国家农业、食品与环境研究院）；University of Montpellier（蒙彼利埃大学）
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classi…
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-SCE32UVSQ, Université Paris Saclay（巴黎萨克雷大学气候与环境科学实验室）；Kayrros SAS（Kayrros SAS公司）；IGN, Laboratoire d’Inventaire Forestier（法国国家地理与森林信息研究所森林清查实验室）；INRAE, UMR TETIS, Université de Montpellier, AgroParisTech（法国国家农业食品与环境研究院、蒙彼利埃大学、巴黎高科农业学院）；Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；IGN, Service de l'Information Statistique, Forestière et Environnementale（法国国家地理与森林信息研究所统计、森林与环境信息服务处）；CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS（图卢兹大学生物圈空间研究中心）；ISPA, UMR 1391, INRA Nouvelle-Aquitaine（法国国家农业研究院新阿基坦大区农业与环境相互作用研究所）
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
School of Resource and Environmental Science, Wuhan University（武汉大学资源与环境科学学院）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Collaborative Innovation Center of Geospatial Technology, Wuhan University（武汉大学地球空间信息技术协同创新中心）；State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
Silesian University of Technology（西里西亚工业大学）；KP Labs（KP实验室）；European Space Agency（欧洲空间局）
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5 m cultivated land maps from 10 m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experimen…
Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping
Department of Geomatics Engineering, University of Calgary（测绘工程系，卡尔加里大学）；Department of Geography, Faculty of Arts, The University of Jordan（地理系，艺术学院，约旦大学）
The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultu…
ICEYE Oy（ICEYE公司）；VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；Aalto University（阿尔托大学）
Land cover mapping is essential to monitoring the environment and understanding the effects of human activities on it. The automatic approaches to land cover mapping (i.e., image segmentation) mostly used traditional machine learning that requires heuristic feature design. On natural images, deep learning has outperformed traditional machine learning approaches for image segmentation. On remote sensing images, recent studies demonstrate successful applications of specific deep learning models to small-scale land cover mapping tasks (e.g., to classify wetland complexes). However, it is not read…
Politecnico di Torino（都灵理工大学）；International Islamic University（国际伊斯兰大学）
The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time consuming solution that poses strict limita…
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
TU Kaiserslautern（凯撒斯劳滕工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an ov…
Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset …
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verifie…
Norwegian Institute for Nature Research - NINA（挪威自然研究所）
Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sen…
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery. While quite some datasets have already been published by the community, most of them suffer from rather strong limitations, e.g. regarding spatial coverage, diversity or simply number of available samples. Exploiting the freely available data acquired by the Sentinel satellites of the Copernicus program implemented by the European Space Agency, as well as the cloud computing facili…
Deep Neural Networks (DNNs) are getting increasing attention to deal with Land Cover Classification (LCC) relying on Satellite Image Time Series (SITS). Though high performances can be achieved, the rationale of a prediction yielded by a DNN often remains unclear. An architecture expressing predictions with respect to input channels is thus proposed in this paper. It relies on convolutional layers and an attention mechanism weighting the importance of each channel in the final classification decision. The correlation between channels is taken into account to set up shared kernels and lower mod…
Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use. Here we present a globally consistent method to identify trees with canopy diameters greater than three me…
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Smith School of Enterprise and the Environment（史密斯企业与环境学院）；School of Geography and the Environment（地理与环境学院）；University of Oxford（牛津大学）
Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to the reporting of companies and countries. In this paper, I introduce DeepSentinel, a data pipeline and experimentation framework for producing general-purpose semantic embeddings of paired Sentinel-1 and S…
Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly …
Tallinn University of Technology（塔林理工大学）；University of Oxford（牛津大学）
The focus of this paper is using a convolutional machine learning model with a modified U-Net structure for creating land cover classification mapping based on satellite imagery. The aim of the research is to train and test convolutional models for automatic land cover mapping and to assess their usability in increasing land cover mapping accuracy and change detection. To solve these tasks, authors prepared a dataset and trained machine learning models for land cover classification and semantic segmentation from satellite images. The results were analysed on three different land classification…
Technical University of Munich（慕尼黑工业大学）；Information Engineering University（信息工程大学）；Ruhr-University Bochum（波鸿鲁尔大学）；German Aerospace Center（德国航空航天中心）
As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environments, especially on a large scale. Remote sensing data-based classification approaches are the key to large-scale mapping and monitoring of LCZs. The potential of deep learning-based approaches is not yet fully explored, even though advanced convolutional neural networks (CNNs) continue to push the frontiers for various computer vision tasks. One reason is that published studies are based on different datasets, usua…
Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
School of Information Technology, Deakin University, Australia（信息技术学院，德堪大学，澳大利亚）；Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Australia（维多利亚农业研究，能源、环境与气候变化行动部，澳大利亚）；Institute of Innovation, Science and Sustainability, Federation University, Australia（创新、科学与可持续性研究所，联邦大学，澳大利亚）
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land use classification by identifying the nature of industrial sites from medium-resolution remote sensing images. In this work, we focus on classifying different types of power plants from Sentinel-2 imaging data. Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class. Furthermore, we are able to identify the cooling me…
Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifi…
Supervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands
ESRIN, European Space Agency（欧洲空间局ESRIN）；Dept. of Aerospace Engineering, University of Naples Federico II（那不勒斯费德里科二世大学航空航天工程系）；University of Amsterdam（阿姆斯特丹大学）
CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification
Instituto Nacional de Pesquisas Espaciais (INPE)（巴西国家空间研究院）；German Aerospace Center (DLR)（德国航空航天中心）；German Society for International Cooperation (GIZ)（德国国际合作机构）
University of Florence（佛罗伦萨大学）；Centre Tecnològic Telecomunicacions Catalunya（加泰罗尼亚电信技术中心）；University of Padua（帕多瓦大学）
In this letter, we use deep-learning convolution neural networks (CNNs) to assess the landslide mapping and classification performances on optical images (from Sentinel-2) and SAR images (from Sentinel-1). The training and test zones used to independently evaluate the performance of the CNNs on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multi-polarization SAR as well as op…
Institute of Astronomy, Astrophysics, Space Applications & Remote Sensing, National Observatory of Athens（雅典国家天文台天文、天体物理、空间应用与遥感研究所）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of the Peloponnese（伯罗奔尼撒大学）
The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set of benchmark experiments is currently lacking, i.e. DL models tested on the same dataset, with a common and consistent set of metrics, and in the same hardware. In this work, we use the BigEarthNet Sentinel-2 dataset to benchmark for the first time different state-of-the-art DL models for the multi-label, multi-class LULC image classification problem, contributing with an exha…
The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become poss…
Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Joint Institute for Regional Earth System Science and Engineering, University of California - Los Angeles（加利福尼亚大学洛杉矶分校区域地球系统科学与工程联合研究所）
本文基于 OPERA RTC-S1 数据训练无需标签的自监督 vision transformer，通过逐像素基线分布偏差检测地表扰动，并在三地灾害事件中取得优于现有方法的结果。
NORCE, The Norwegian Research Centre AS（挪威研究中心NORCE）；UiT the Arctic University of Norway（挪威北极大学UiT）
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ERA5 dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, …
Forests are vital for the wellbeing of our planet. Large and small scale deforestation across the globe is threatening the stability of our climate, forest biodiversity, and therefore the preservation of fragile ecosystems and our natural habitat as a whole. With increasing public interest in climate change issues and forest preservation, a large demand for carbon offsetting, carbon footprint ratings, and environmental impact assessments is emerging. Most often, deforestation maps are created from optical data such as Landsat and MODIS. These maps are not typically available at less than annua…
Instituto Tecnológico de Mazatlan（马萨特兰理工学院）；Instituto Politecnico Nacional（国立理工学院）；CICATA-Qro（克雷塔罗应用科学与技术研究中心）；Universidad Autónoma de Querétaro（克雷塔罗自治大学）
Université de Bretagne Sud（南布列塔尼大学）；IRISA（法国信息与随机系统研究所）；CNES（法国国家空间研究中心）；Thales Alenia Space（泰雷兹阿莱尼亚宇航公司）
In the field of remote sensing and more specifically in Earth Observation, new data are available every day, coming from different sensors. Leveraging on those data in classification tasks comes at the price of intense labelling tasks that are not realistic in operational settings. While domain adaptation could be useful to counterbalance this problem, most of the usual methods assume that the data to adapt are comparable (they belong to the same metric space), which is not the case when multiple sensors are at stake. Heterogeneous domain adaptation methods are a particular solution to this pr…
Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentine…
