多光谱影像星上压缩方法探讨

在星地数据传输信道带宽有限的情况下,为提高星载影像数据的单轨下传量,科研人员已开展卫星影像数据星上压缩方法的研究工作。区别于全色影像的压缩,多光谱或高光谱影像的星上压缩不仅需要考虑诸如JPEG等基于空间相关性的压缩算法,还需要结合如PCA等基于谱间相关性的方法,从而在数据压缩过程中保证空间和光谱两方面的信息保真度。因此,本文将回顾目前基于空间变换和谱间变换,以及基于内容的遥感影像压缩方法。进一步针对常规性监测和灾害应急监测两种需求,在考虑星上数据预处理和智能处理的基础上,提出了一种多光谱或高光谱影像星上压缩的理论框架及其对应的地面数据处理流程。最后,本文指出了多光谱或高光谱影像星上压缩研究工作中需进一步探讨的若干技术问题。     

A GIS approach to ingest Meteosat Second Generation data into the Local Analysis and Prediction System

The Local Analysis and Prediction System (LAPS) is modified to ingest Meteosat Second Generation (MSG) data for cloud analysis. A first study is conducted to test the actual performance of the weather analysis software after new satellite bands are introduced. Results show that the system provides high quality cloud products such as cloud mask, cloud top height and cloudiness. A comparison with products from EUMETSAT’s Nowcasting SAF shows a general underestimation of the LAPS product although the results are not conclusive. The study shows the potential of MSG data in refining the mesoscale analyses produced by LAPS. Moreover the software tools, based on open source codes for geolocation and geographical information systems, written for the transformation of MSG data into input files for LAPS have demonstrated a great flexibility and ease of use. The study opens up an avenue for successive validation and refinement of the analyses together with their improved implementation for operational nowcasting and very short range forecasting applications.     

基于MODIS多通道合成的江淮暴雨云系分析方法

卫星多通道合成能直观反映出卫星云图上的一些特性,通过采用经过预处理后的MODIS多通道数据,利用“白天自然色”、“白天微物理”、“白天太阳”和“空气团”4种多通道组合,结合实况降水分布和地面气象观测站点资料,以2010年7月13日江淮流域的一次特大暴雨过程为例,定性分析暴雨云系微观物理性质,推断云粒子大小和相态等,并对比高时空分辨率的局地分析预报系统(LAPS)中尺度物理量场以及不同研究个例的多通道合成图分析。结果表明：卫星多通道RGB合成图能以色彩的形式有针对性地突出对流系统、云粒子微观物理性质等属性,具有一定的精确度和普适性,有利于暴雨等中尺度强对流天气的监测。     

A comparative study of libRadtran and RTTOV predicted satellite images using the WRF forecasting output

The assimilation of cloudy radiances remains important in improving precipitation and severe weather forecasting. In practice, Numerical Weather Prediction (NWP) Models frequently do not predict meso-scale phenomena, so the phenomenon is either predicted but not realised, or is well predicted but not where it is observed. Radiative Transfer Models such as TIROS-Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV) and libRadtran are the mathematical operators used in the simulation of satellite data. In the data assimilation process, an effective reproduction of the mesoscale convective phenomenon leads to high quality data analysis. Therefore, we are looking for an operator that reproduces the NWP model’s behaviour in a realistic way. Several cloud parameterisation schemes are available in RTTOV and libRadtran to simulate the satellite cloudy radiances. Each selected scheme may result in different simulated brightness temperature data compared to those observed by satellite. However, the source of errors is still unknown: are they generated by the RT model, are they coming from the predicted fields of the NWP Model used as input, or from both? This study aims to investigate the impact of libRadtran or RTTOV operators on the quality of the predicted satellite image. The same mandatory forecasted fields are used as input for both models and derive from the Weather Research and Forecasting (WRF) Limited-Area Model. In this study, we did not investigate the total capability of RT models, but we have focused on a standard and specific physical parameterisation scheme. As reference data, Meteosat Second Generation (MSG) images have been used to compute the deterministic and probabilistic scores. The results for the deterministic scores show that libRadtran reproduces the cold temperatures predicted by WRF well, but these are sometimes slightly distant from their geographical location. Conversely, for the RTTOV, there is a tendency to miss more cases of good detection of the events predicted by WRF. Probabilistic analyses confirm an improvement in libRadtran scores when the neighbourhood size is increased, and a boxplot analysis of the bootstrap method confirms the stability of scores for both models.     

Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.     

Tidal streams observed with sequential AVHRR images

Ten consecutive Advanced Very High Resolution Radiometer (AVHRR) infrared images off the north-west coast of Australia were analysed to detect tidal motions by tracking surface features using the maximum cross-correlation (MCC) method. The study area contains a broad continental shelf with the 200?m isobath situated approximately 200?km offshore. Tidal height variations at the coast are in excess of 8?m. The results not only demonstrate the potential of the MCC method in defining surface displacements in coastal regions but also provide a warning to MCC users that tidal effects must be taken into account when deriving mesoscale ocean currents from satellite sensor imagery.     

Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8–3.9, 6.2–10.8, 8.7–10.8, and 10.8–12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8–3.9 and 8.7–10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2–10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively.     

Automatic mapping of soil texture through the integration of ground,satellite and ancillary data

Satellite images obtained in the optical domain can provide information on important soil properties, such as texture. The use of these images to automatically map soil texture is, however, complicated by the presence of vegetation cover, which can mask the soil spectral response. A multistep methodology based on the use of ground, satellite and ancillary data is proposed and tested to map soil texture in Grosseto, a province of Central Italy. The methodology first separated vegetated and nonvegetated pixels of Landsat Thematic Mapper (TM) images by the use of an appropriate spectral index, the Soil Adjusted Vegetation Index (SAVI). Next, different transforms (nonparametric and parametric) were tuned using ground samples and applied to the two pixel types to separately extract relevant spectral information. The outcomes of these transforms were then merged and subjected to further processing aimed at reducing noise and conveying spatial information to the mapping process. The stratification of the soil texture estimates obtained on different lithological units was finally tested to further improve map accuracy.     

A study of cloud classification with neural networks using spectraland textural features

The problem of cloud data classification from satellite imagery using neural networks is considered. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.     

改进卷积网络的高分遥感图像城镇建成区提取

目的 城镇建成区是城镇研究重要的基础信息,也是实施区域规划、落实城镇功能空间布局的前提。但是遥感影像中城镇建成区的环境复杂,同时不同城镇建成区在坐落位置、发展规模等方面存在许多差异,导致其信息提取存在一定困难。方法 本文基于面向图像语义分割的深度卷积神经网络,使用针对特征图的强化模块和通道域的注意力模块,对原始DeepLab网络进行改进,并通过滑动窗口预测、全连接条件随机场处理方法,更准确地实现城镇建成区提取。同时,针对使用深度学习算法容易出现过拟合和鲁棒性不强的问题,采用数据扩充增强技术进一步提升模型能力。结果 实验数据是三亚和海口部分地区的高分二号遥感影像。结果表明,本文方法的正确率高于93%,Kappa系数大于0.837,可以有效地提取出大尺度高分辨率遥感影像中的城镇建成区,且提取结果最为接近实际情况。结论 针对高分辨率遥感卫星影像中城镇建成区的光谱信息多样化、纹理结构复杂化等特点,本文算法能在特征提取网络中获取更多特征信息。本文使用改进的深度学习方法,提出两种处理方法,显著提高了模型的精度,在实际大幅遥感影像的使用中表现优秀,具有重要的实用价值和广阔的应用前景。     

Spatio-spectral fusion of satellite images based on dictionary-pair learning

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.     

中尺度涡特征提取算法及可视化研究

在对海洋遥感图像数据进行处理,特征可视化检测海洋信息的过程中,中尺度涡信息检测是重要内容之一,其结果直接影响海洋中尺度涡信息反演的精度.为了提高中尺度涡信息提取的准确性,本文根据海洋中尺度涡成像的特点,基于边缘检测,运用连通区域提取技术,综合利用涡旋的形状、尺度等判据,检测图像中的旋涡闭合等值线以及提取旋涡的特征参数.采用Matlab和C#混合编程,将中尺度涡检测算法和遥感数据信息有机结合起来,实现了中尺度涡检测的自动化和可视化,提高了遥感图像的处理效率.实验表明,该方法具有较高的中尺度涡信息检测精度,检测效果理想.     

Detecting and tracking mesoscale precipitating objects using machine learning algorithms

Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines (SVMs), Decision Trees (DT), and Random Forests (RD) algorithms were applied to extract and track mesoscale convective precipitating clouds from a series of 22 Geostationary Operational Environmental Satellite-13 meteorological image sub-scenes over the continental territory of Colombia. This study’s aims are twofold: (i) to establish whether the use of five meteorological spectral channels, rather than a single infrared (IR) channel, improves rainfall objects detection and (ii) to evaluate the potential of machine learning algorithms to locate precipitation clouds. Results show that while the SVM algorithm provides more accurate classification of rainfall cloud objects than the traditional IR brightness temperature threshold method, such improvement is not statistically significant. Accuracy assessment was performed using STEP (shape (S), theme (T), edge (E), and position (P)) object-based similarity matrix method, taking as reference precipitation satellite images from the Tropical Rainfall Measuring Mission. Best thematic and geometric accuracies were obtained applying the SVM algorithm.     

A software tool for monitoring the features of convective cloud systems with the use of Meteosat images

Automatic tracking and characterization of convective systems on Meteosat images is of great importance not only for monitoring system development but also for short-time prediction of the system behavior. In this study, a software tool has been developed for the monitoring of mesoscale convective systems (MCSs) linked with heavy rain in Greece, using as input Meteosat infrared and water vapor images. The software is capable of estimating and monitoring, in real time, the characteristics of propagating convective systems with respect to cloud system potential, cloud geometric features and cloud system motion. Using this software tool, an extensive cloud system connected with heavy rainfall in Greece is examined and assessed.     

Comparison of different pan-sharpening methods for spectral characteristic preservation: multi-temporal CARTOSAT-1 and IRS-P6 LISS-IV imagery

A host of remote-sensing and mapping applications require both high spatial and high spectral resolutions. Availability of high spatial and spectral details at different resolutions from a suite of satellite sensors has necessitated the development of effective image fusion techniques that can effectively combine the information available from different sensors and take advantage of their varied capabilities. A common problem observed in the case of multi-sensor multi-temporal data fusion is spectral distortion of the fused images. Performance of a technique also varies with variation in scene characteristics. In this article, two sets of multi-temporal CARTOSAT-1 and Indian Remote Sensing satellite (IRS-P6) Linear Imaging and Self Scanning sensor (LISS-IV) image sub-scenes, with different urban landscape characteristics, are fused with an aim to evaluate the performance of five image fusion algorithms – high-pass filtering (HPF), Gram–Schmidt (GS), Ehlers, PANSHARP and colour-normalized Brovey (CN-Brovey). The resultant fused data sets are compared qualitatively and quantitatively with respect to spectral fidelity. Spatial enhancement is assessed visually. The difference in the performance of techniques with variation in scene characteristics is also examined. For both scenes, GS, HPF and PANSHARP fusion techniques produced comparable results with high spectral quality and spatial enhancement. For these three methods, the variation in performance over different scenes was not very significant. The Ehlers method resulted in spatially degraded images with a more or less constant negative offset in data values in all bands of one scene and in the first two bands in the other. The CN-Brovey method produced excellent spatial enhancement but highly distorted radiometry for both sub-scenes.     

Fusion of multisensor multitemporal satellite data for land cover mapping

Efficient integration of remote sensing information with different temporal, spectral and spatial resolutions is important for accurate land cover mapping. A new temporal fusion classification (TFC) model is presented for land cover classification, based on statistical fusion of multitemporal satellite images. In the proposed model, the temporal dependence of multitemporal images is taken into account by estimating transition probabilities from the change pattern of a vegetation dynamics indicator (VDI). Extension of this model is applicable to Synthetic Aperture Radar (SAR) images and integration of multisensor multitemporal satellite images, concerning both temporal attributes and reliability of multiple data sources. The feasibility of the new method is verified using multitemporal Landsat Thematic Mapper (TM) and ERS SAR satellite images, and experimental results show improved performance over conventional methods.     

Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.     

黑河中游张掖绿洲灌溉渠系的数字化制图与结构分析

人工灌溉渠系对于干旱区内陆河绿洲的生存和发展具有重要作用。以黑河中游的张掖绿洲为例,在收集大量高分辨率遥感影像和地形图资料的基础上,利用GPS实地测量和GIS软件提取了全绿洲干、支、斗3级渠系信息,获得了翔实准确的灌溉渠系空间和属性数据,首次完成了张掖绿洲灌溉渠系的数字化制图并对该渠系网络的空间格局进行了分析。结果表明：张掖绿洲目前渠道总数约为6 300条,总长为8 749.51 km,密度为0.47 km/km²,干、支、斗渠的比例为1∶1.17∶2.4。5个县区中甘州区的灌溉渠系分布最密集,而山丹县渠系建设相对落后。 绿洲人工灌溉渠系建设方式和水资源利用开发程度是影响和改变本地区流域景观结构和土地利用方式的重要因素。     

Granular neural networks for land use classification

Granulation of information is a new way to describe the increased complexity of natural phenomena. The lack of clear borders in nature calls for a more efficient way to process such data. Land use both in general but also as perceived in satellite images is a typical example of data that are inherently not clearly delimited. A granular neural network (GNN) approach is used here to facilitate land use classification. The GNN model used combines membership functions of spectral as well as non-spectral spatial information to produce land use categories. Spectral information refers to IRS satellite image bands and non-spectral data are here of topographic nature, namely slope, aspect and elevation. The processing is done through a standard neural network trained by back-propagation learning algorithm. A thorough presentation of the results is given in order to evaluate the merits of this method.