遥感大数据研究现状与发展趋势

目的 遥感数据空间分辨率、时间分辨率、光谱分辨率以及辐射分辨率不断提高,数据类型也不断增加,从航天、航空、临近空间等遥感平台所获取的遥感数据量急剧增加,遥感数据已经具有明显的大数据特征。本文旨在从系统应用的角度分析遥感大数据处理中涉及的关键技术与问题,为相关研究人员提供有价值的参考。方法 在参考大量文献的基础上,首先阐明遥感大数据的特点。其次,从GPU硬件加速、集群、网格、云计算、云格、复杂高性能计算等角度介绍了遥感大数据处理系统。再次,从分布式集群化存储技术等,分析了遥感大数据处理的关键技术。最后,从遥感大数据的多类不确定性、信息融合、机器学习、分析平台等出发,说明了目前研究存在的问题;从遥感大数据多类不确定性建模,面向遥感大数据的机器学习方法等角度说明了遥感大数据发展的趋势。结果 本文详细梳理了遥感大数据的特点、典型的处理系统、核心技术,力图总结出在实际应用与学术研究中该领域需要解决的关键问题以及未来的发展趋势。结论 大数据技术为遥感数据挖掘与知识获取带来了机遇与挑战,面向大数据的机器学习、数据统一分析框架、面向大数据的信息深度融合等问题的突破,将促进遥感知识挖掘的进一步发展。     

基于多源遥感数据融合的土壤水分反演研究

土壤含水量作为地表的重要参量之一,对地球能量循环、水循环、碳循环及生态环境都有十分重要的意义。以南京市金川河流域为研究区,融合哨兵 2 号 L2A 数据和 Landsat 8 遥感数据 2 种数据源,分别采用偏最小二乘法(PLSR)、最小二乘-支持向量机(LS-SVM)、反向传播神经网络(BPNN)和随机森林(RF)4 种建模方法,建立遥感数据与土壤含水量之间的关系,并进行模型的验证与评价。结果表明：1)土壤含水量与哨兵 2 号和 Landsat 8 各波段反射率均呈负相关关系,和海岸带监测波段(波长为 430~450 nm)和近红外波段(波长为 2 100~2 300 nm)相关性最佳;2)融合后的遥感数据相较于单一遥感数据源,预测土壤含水量的能力更佳, 最优模型 R2 达 0.996,均方根误差仅为 0.003 g/g;3)4 种建模方法中,建模效果从好到差依次为 PLSR,RF, LS-SVM,BPNN。融合哨兵 2 号 L2A 和 Landsat 8 数据,结合 PLSR 建模方法可进行土壤含水量的精准反演, 相较于现有研究反演精度大大提升,对研究该地区地表与地下水循环和生态环境治理有一定参考价值。     

A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data

A multi-spectral non-local (MSN) method is developed for advanced retrieval of boundary layer cloud properties from remote sensing data, as an alternative to the independent pixel approximation (IPA) method. The non-local method uses data at both the target pixel and neighboring pixels to retrieve cloud properties such as pixel-averaged cloud optical thickness and effective droplet radius. Radiance data to be observed from space were simulated by a three-dimensional (3D) radiation model and a stochastic boundary layer cloud model with two-dimensional (horizontal and vertical) variability in cloud liquid water and effective radius. An adiabatic assumption is used for each cloud column to model the geometrical thickness and vertical profiles of cloud liquid water content and effective droplet radius, neglecting drizzle and cloud brokenness for simplicity. The dependence of radiative smoothing and roughening on horizontal scale, optical thickness and single scattering albedo are investigated. Then, retrieval methods using 250-m horizontal resolution data onboard new generation satellites are discussed. The regression model for the MSN method was trained based on datasets from numerical simulations. The training was performed with respect to various domain averages of optical thickness and effective radius, because smoothing and roughening effects are strongly dependent on the two variables. Retrieval accuracy is discussed here with datasets independent of those used in the training, towards assessing the generality of the technique. It is demonstrated that retrieval accuracy of cloud optical thickness, which is often retrieved from single-spectral visible-wavelength data, is improved the most using neighboring pixel data and secondly using multi-spectral data, and ideally with both. When the IPA retrieval method is applied to optical thickness and effective radius, the root-mean-square relative errors can be 15-90%, depending on solar and view directions. In contrast, the MSN method has errors of 4-10%, which is smaller than IPA by a factor of 2-10. It is also suggested that the accuracy of the MSN method is insensitive to some assumptions in the inhomogeneous cloud input data used to train the regression model.     

Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks

Retrieval of the biomass parameters from active/passive microwave remote sensing data is performed based on an iterative inversion of the artificial neural network (ANN). The ANN is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the ANN training is complete, the ANN can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The retrieved biomass include canopy height, canopy water content and dry matter fraction, and the wetness of the underlying land. Two examples for wheat and oat are illustrated. The retrieved biomass parameters agree well with the real data of the ground truth.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.     

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

Due to the noise that is present in remote sensing data, a robust method to retrieve information is needed. In this study, the active learning method (ALM) is applied to spectral remote sensing reflectance data to retrieve in‐water pigment. The heart of the ALM is a fuzzy interpolation method that is called the ink drop spread (IDS). Three datasets (SeaBAM, synthetic and NOMAD) are used for the evaluation of the selected ALM approach. Comparison of the ALM with the ocean colour 4 (OC4) algorithm and the artificial neural network (ANN) algorithm demonstrated the robustness of the ALM approach in retrieval of in‐water constituents from remote sensing reflectance data. In addition, the ALM identified and ranked the most relevant wavelengths for chlorophyll and pigment retrieval.     

面向大数据分析的在线学习算法综述

点对学习（pairwise learning）是指损失函数依赖于2个实例的学习任务. 遗憾界对点对学习的泛化分析尤为重要. 现有的在线点对学习分析只提供了凸损失函数下的遗憾界. 为了弥补非凸损失函数下在线点对学习理论研究的空白,提出了基于稳定性分析的非凸损失函数在线点对学习的遗憾界. 首先提出了一个广义的在线点对学习框架,并给出了具有非凸损失函数的在线点对学习的稳定性分析;然后,根据稳定性和遗憾界之间的关系,对非凸损失函数下的遗憾界进行研究;最后证明了当学习者能够获得离线神谕（oracle）时,具有非凸损失函数的广义在线点对学习框架实现了最佳的遗憾界$O({T^{ - 1/2}})$.

     

基于微波和可见光遥感的冬小麦土壤墒情反演

土壤墒情是水文学、气象学及农业科学研究领域中的一个重要指标参数,对气候、农业、旱情监测都具有极为重要的意义。以河南省广利灌区冬小麦为研究对象,以水云模型为基础,利用Landsat-8和Sentinel-1 A数据计算双层衰减因子、植被和土壤的后向散射系数。采用RBF神经网络拟合土壤的后向散射系数与土壤含水量的关系,通过实测监测点对反演的结果进行精度验证,结果表明:相关系数的平方为0.754 5,均方根误差为0.022,结果良好,可为灌区冬小麦土壤墒情的评估提供参考。     

面向遥感影像的深度语义哈希检索

目的 哈希检索旨在将海量数据空间中的高维数据映射为紧凑的二进制哈希码,并通过位运算和异或运算快速计算任意两个二进制哈希码之间的汉明距离,从而能够在保持相似性的条件下,有效实现对大数据保持相似性的检索。但是,遥感影像数据除了具有影像特征之外,还具有丰富的语义信息,传统哈希提取影像特征并生成哈希码的方法不能有效利用遥感影像包含的语义信息,从而限制了遥感影像检索的精度。针对遥感影像中的语义信息,提出了一种基于深度语义哈希的遥感影像检索方法。方法 首先在具有多语义标签的遥感影像数据训练集的基础上,利用两个不同配置参数的深度卷积网络分别提取遥感影像的影像特征和语义特征,然后利用后向传播算法针对提取的两类特征学习出深度网络中的各项参数并生成遥感影像的二进制哈希码。生成的二进制哈希码之间能够有效保持原始高维遥感影像的相似性。结果 在高分二号与谷歌地球遥感影像数据集、CIFAR-10数据集及FLICKR-25K数据集上进行实验,并与多种方法进行比较和分析。当编码位数为64时,相对于DPSH（deep supervised Hashing with pairwise labels）方法,在高分二号与谷歌地球遥感影像数据集、CIFAR-10数据集、FLICKR-25K数据集上,mAP（mean average precision）指标分别提高了约2%、6%7%、0.6%。结论 本文提出的端对端的深度学习框架,对于带有一个或多个语义标签的遥感影像,能够利用语义特征有效提高对数据集的检索性能。     

RETRACTED ARTICLE: Image pattern recognition in big data: taxonomy and open challenges: survey

Multimedia Tools and Applications - Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science...     

Debating big data: A literature review on realizing value from big data

Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model.     

EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.     

A novel retrieval method for remote sensing image based on statistical model

With the increasing number of high-resolution remote sensing (HRRS) image technologies, there is an interest in seeking a way to retrieve images efficiently. In order to describe the images with abundant texture information more concisely and accurately, we propose a novel remote sensing image retrieval approach based on the statistical features of non-subsampled shearlet transform (NSST) coefficients, according to which we set up a model using Bessel K form (BKF). First, the remote sensing (RS) image is decomposed into several subbands of frequency and orientation using the non-subsampled shearlet transform. Then, we use the Bessel K distribution model is utilized to describe the coefficients of NSST high-frequency subband. Next, the BKF parameters are selected to serve as the texture feature to represent the characteristics of image, namely BKF statistical model feature (BSMF), and the feature vector of each image is created by combination with parameters at each high-pass subband. Both the experiment and theory indicate that the BKF distribution is highly matched with the statistical features of NSST coefficients within high-pass subbands. In our experiments, we applied the proposed method to two general RS image datasets- The UC Merced land use dataset and the Sydney dataset. The results show that our proposed method can achieve a more robust and commendable performance than the state-of-the-art approaches.     

Attention boosted bilinear pooling for remote sensing image retrieval

ABSTRACT

Remote sensing image retrieval is to find the most identical or similar images to a query image in the vast archive of remote sensing images. A key process is to extract the most distinctive features. In this study, we introduce a second-order pooling named compact bilinear pooling (CBP) into convolutional neural networks (CNNs) for remote sensing image retrieval. The retrieval algorithm has three stages, pretraining, fine-tuning and retrieval. In the pretraining stage, two classic CNN structures, VGG16 and ResNet34, are pretrained respectively with the ImageNet consisting of close-range images. A CBP layer is introduced before the fully connected layers in the two networks. To extract globally consistent representations, a channel and spatial integrated attention mechanism is proposed to refine features from the last convolution layer and the features are used as the input of the CBP. In the fine-tuning stage, the new network is fine-tuned on a remote sensing dataset to train discriminable features. In the retrieval stage, the network, with fully connected layers being replaced by a PCA (principal component analysis) module, is applied to new remote sensing datasets. Our retrieval algorithm with the combination of CBP and PCA obtained the best performance and outperformed several mainstream pooling or encoding methods such as full-connected layer, IFK (Improved Fisher Kernel), BoW (Bag-of-Words) and maxpooling, etc. The channel and spatial attention mechanism contributes to the CBP based retrieval method and obtained the best performance on all the datasets, as well as outperformed several recent attention methods. Source code is available at http://study.rsgis/whu.edu.cn/pages/download.     

电网遥信大数据智能分析辅助决策系统建设研究

电力调度控制中心为适应电网规模不断扩大、结构日趋复杂化而带来的海量设备监控信息,充分发挥现代化技术手段应用的先进作用,提高调控运行人员对电网驾驭能力以及对电网异常、事故信息处置效率,降低人员劳动强度,开展了电网遥信大数据智能分析辅助决策系统建设研究。本研究基于实际电网模型和实时数据,从电网海量信息中梳理、智能分析,提供结论性结果,智能替代人工,而且以电网实时信息为输入,以设备基础信息为辅助,横向对比,纵向分析,提供科学的辅助决策,全程信息深加工,独立设置,只提取系统信息,而不改变系统数据。建成一个具有简、全、独立安全三个主要特点的系统,有效提高电网设备异常事故处置效率、降低安全生产隐患,保证电网安全稳定运行。     

Volcano monitoring using remote sensing data

Abstract

The paper brings out the theoretical basis and utility of near-infrared band data sets obtained from Earth resources satellites, in the estimation of very high temperatures witnessed during volcanic eruptions. The Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning System (LISS-II) data sets for the period 4 April 1991-4 August 1991 were used for studying the volcanic eruption at Barren Island (India). The effect of the sub-pixel size vent in the estimation of pixel-integrated temperature has been discussed. The volcanic vent temperature on 6 May 1991 was found to be 1084K. The availability of mid-IR bands (1-55-1-75 μm and 2.08-2.35μm spectral region) in Landsat-TM enabled bringing out the vent region in the false colour composite (FCC) generated using these and the near-IR (0.76-0.90μm) band. The very high or saturated values in mid-IR bands brought a good contrast between vent and its surroundings     

Image fusion techniques for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the synthetic aperture radar (SAR) interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter (MKF). Each study case presents also the results achieved by the proposed techniques applied to real data.     

Verifying remote data integrity in peer-to-peer data storage: A comprehensive survey of protocols

This paper surveys protocols that verify remote data possession. These protocols have been proposed as a primitive for ensuring the long-term integrity and availability of data stored at remote untrusted hosts. Externalizing data storage to multiple network hosts is becoming widely used in several distributed storage and P2P systems, which urges the need for new solutions that provide security properties for the remote data. Replication techniques cannot ensure on their own data integrity and availability, since they only offer probabilistic guarantees. Moreover, peer dynamics (i.e., peers join and leave at any time) and their potential misbehavior (e.g., free-riding) exacerbate the difficult challenge of securing remote data. To this end, remote data integrity verification protocols have been proposed with the aim to detect faulty and misbehaving storage hosts, in a dynamic and open setting as P2P networks. In this survey, we analyze several of these protocols, compare them with respect to expected security guarantees and discuss their limitations.     

A taxonomy and survey of big data in social media

Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high-quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data-trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1-score, and 5% run time complexity.     

Automatic extraction and delineation of single trees from remote sensing data

In this paper, we present a novel approach for the automatic extraction of trees and the delineation of the tree crowns from remote sensing data, and report and evaluate the results obtained with different test data sets. The approach is scale-invariant and is based on co-registered colour-infrared aerial imagery and a digital surface model (DSM). Our primary assumption is that the coarse structure of the crown, if represented at the appropriate level in scale-space, can be approximated with the help of an ellipsoid. The fine structure of the crown is suppressed at this scale level and can be ignored. Our approach is based on a tree model with three geometric parameters (size, circularity and convexity of the tree crown) and one radiometric parameter for the tree vitality. The processing strategy comprises three steps. First, we segment a wide range of scale levels of a pre-processed version of the DSM. In the second step, we select the best hypothesis for a crown from the overlapping segments of all levels based on the tree model. The selection is achieved with the help of fuzzy functions for the tree model parameters. Finally, the crown boundary is refined using active contour models (snakes). The approach was tested with four data sets from different sensors and exhibiting different resolutions. The results are very promising and prove the feasibility of the new approach for automatic tree extraction from remote sensing data. The major part of this work was carried out while both authors worked together at the Institute of Photogrammetry and GeoInformation, University of Hannover.