Textural Classification of Single Band SIR‐B Data over a Part of Brahmaputra Basin,India

Abstract

Texture is an important spatial feature, useful for identfying objects of regions of interest in an image. There are a number of methods for identification of textural parameters e.g. edgeness, frequency domain analysis, gray tone cooccurrence approach etc. Geologic information in radar images of heavily vegetated areas is contained mostly in the depiction of topography in image texture. Single band SIR/ERS‐1 SAR data posses a problem to the analyst for classification of the various land use/geological classes and generally multidate SAR data are used due to paucity of more number of bands. However, the multidate SAR data classification is not an ideal technique. A new technique, namely, spatial frequency band pass classification technique which generates two or more bands in the Fourier domain using the single band SAR data and then classifies various features of interest using their textural properties has been described here. Result shows higher percentage of classification using Maximum‐Likelihood Classifier (MXL) with two split‐band data as compared to the unsupervised classification of all the bands.     

A Neural Network Landuse Classifier for SAR Images Using Textural and Fractal Information

Abstract

Artificial neural networks (ANN) have recently been popularly used in image classification. Input features to most ANNs are extracted based on a one class per pixel basis. This requires a large number of training samples and thus a slow training rate. In this paper, we describe the use of a windowing technique to extract textural features such as average intensity, second moment of intensity histogram and fractal surface dimension from an image. This method of image characterization reduces the number of training samples efficiently, yet retains a reasonable overall classification accuracy. The ANN is trained based on the back‐error propagation algorithm. The method is applied for landuse classification of Synthetic Aperture Radar (SAR) images. An example is given for a site in Kedah State, Malaysia. The SAR images (HH,HV,VV) were taken by the Canadian Centre for Remote Sensing (CCRS) CV‐580 airborne C‐band SAR system in November 1993 during their GlobeSAR mission in Malaysia. These multi‐polarization SAR images are co‐registered with a Landsat Thematic Mapper (TM) channel 5 image from same area. An overall classification accuracy of about 86.95% is achieved using windowing technique, as compared to 68.22% based on one class per pixel approach. This shows that through fractal and textural information, the windowing technique when applied in an ANN classifier has a great potential in remote sensing applications.     

用SAR与TM数字复合图像编制1:5万～1:10万解译地质图的优点及精度分析

SAR与TM数字复合处理技术,能为地质解译及地质制图提供一份空间与波谱信息都比较丰富 的图像。复合图像的优点是：地物细节详尽,立体感强,图像上阴影又少,便于进行各种图像增 强处理和与其他地学资料对比分析、拟合。试验表明,除岩性解译能力较差外,其它地质体的解译能完全或基本满足中等(大)比例尺区域地质调查的要求。     

基于小波纹理信息的星载SAR图像与TM图像的数据融合

遥感图像的数据融合是当前遥感界研究的热点问题之一。论述利用小波变换提取合成孔径雷达（SAR）图像的多尺度纹理信息，基于小波纹理信息将SAR图像与TM图像进行融合。选取徐州市南郊风景区的Radarsat卫星SAR图像和TM图像进行试验研究，并与颜色变换法融合图像进行对比分析，结果表明，无论是目视解译还是定量分析，该融合方法与颜色变换法相比，将获得更理想的高空间分辨率多光谱的融合图像。     

SAR image classification based on its texture features

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation. It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.     

SAR image classification based on its texture features

SAR images not only have the characteristics of all-ay, all-eather, but also provide object infor-mation which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The au-thors conducted the experiments of texture statistics analysis on SAR im-age features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpreta-tion.     

A Robust Texture Analysis and Classification Approach for Urban Land‐Use and Land‐Cover Feature Discrimination

Abstract

Attempts to analyze urban features and to classify land use and land cover directly from high‐resolution satellite data with traditional computer classification techniques have proven to be inefficient for two primary reasons. First, urban landscapes are composed of complex features. Second, traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture plays an important role in image segmentation and object recognition, as well as in interpretation of images in a variety of applications. This study analyzes urban texture features in multi‐spectral image data. Recent developments in the very powerful mathematical theory of wavelet transforms have received overwhelming attention by image analysts. An evaluation of the ability of wavelet transform in urban feature extraction and classification was performed in this study, with six types of urban land cover features classified. The preliminary results of this research indicate that the accuracy of texture analysis in classifying urban features in fine resolution image data could be significantly improved with the use of wavelet transform approach.     

高分三号卫星全极化SAR影像九寨沟地震滑坡普查

基于光学遥感影像的区域滑坡普查易受云雾天气的影响,存在滑坡体调查不全面的问题,无法满足震后应急调查与恢复重建的需求。本文提出了一种极化SAR卫星数据滑坡普查方法,采用高分三号全极化SAR卫星影像数据,以九寨沟地震震区为实验区,在深入分析滑坡体和其他地物类型散射特征的基础上,融合极化特征、纹理特征和地形特征等多维特征信息,结合高分二号影像获取的训练样本,构建基于BP神经网络的全极化SAR数据滑坡自动识别模型,实现滑坡体的自动快速识别。与高分辨率光学影像与无人机航空影像目视解译结果相比较,总体识别精度为92.8%,Kappa系数为0.715,识别准确度满足地震应急实际应用的需求。研究成果可用于震区大区域滑坡体的普查,为后续开展无人机高分辨率影像滑坡体详查、灾后应急与景区恢复提供辅助信息支撑,并促进国产高分SAR卫星数据在防震减灾中的应用。     

Comparison and Integration of Radar and Optical Data for Land Use/Cover Mapping

Abstract

This study examined the complementarity of spaceborne radar and optical data for surface feature identification. RADARSAT data sets were assessed independently and in combination with Landsat Thematic Mapper (TM) multispectral data. The primary methodology was spectral signature extraction and the application of a statistical decision rule to classify the surface features for a site near Kericho, Kenya. Relative accuracy of the resultant classifications was established by digital integration and comparison to reference information derived from field visitation. Speckle filtering was a great improvement over the poor results achieved with the unfiltered, original radar data but still not adequate for accurate land cover classification. The extraction and use of Variance texture measures was found to be very advantageous. The overall results were not significant improvements over speckle removal (6% increase) but several individual classes, forest and urban, had excellent results with texture. Combinations of radar with Landsat TM greatly improved results, achieving near perfect classification of all individual classes. The highest overall accuracy was achieved with a merger that included the best individual texture image and six reflectance bands of the TM data. The systematic strategy of this study, determination of the best individual method before introducing the next procedure, was effective in managing a very complex, almost infinite set of analysis possibilities.     

Evaluation and integration of ERS-1-SAR and optical sensor data (TM and IRS) for geological investigations

The C-band imaging radar of ERS-1, due to its high sensitivity to terrain surface features, holds tremendous potential in topographic terrain mapping for various applications. This is being examined for geological applications, mainly structural and lithological mapping in a mineral belt of Bihar and Orissa, India. The high image contrast that facilitates structural interpretation and highlights topography on the SAR images, reflects the high sensitivity of the ERS-1-SAR to change in terrain slope in the study area. Extensive lineaments, fold structure and major lithological contacts are easily mappable from the SAR imagery. Many of the lineaments, lithological contacts and fold pattern are mapped equally from optical data (Landsat-TM and IRS-1B FCC). The close association of fold pattern and mineral deposits in the region has necessitated the study of those structures carefully from various remote sensing data products. Synergism between SAR and TM provided useful results regarding structure and lithology of the region. The advantage of SAR in highlighting topography and detecting lineaments are affected to a great extent by the speckle noise and low pixel resolution. The present study shows that future geologic interpretation demands high spatial resolution and efficient data processing technique which reduces the speckle noise more significantly.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

Earth observations during space shuttle mission STS‐34: 18–23 October 1989

Abstract

Although high‐resolution microwave synthetic aperture radar (SAR) sensors possess all‐weather capability for mapping soil moisture from spaceborne platforms, continuous temporal and spatial monitoring of this important hydrological parameter has been relatively limited. However, the recent launch of operational SAR sensors aboard various satellites have made possible synoptic soil moisture monitoring a reality. Such systems operate over a wide range of frequencies, look angles, and polarization combinations, and thus show synergistic advantages when combined for estimating soil moisture patterns. Two soil moisture inversion algorithms have been developed using as inputs radar backscattering data at L, S, and C bands in the microwave frequency range. These models have been tested using radar image simulation with speckle added. It is observed that the neural network algorithm yields superior results in mapping actual soil moisture patterns over the linear statistical inversion technique, although both models show comparable errors in soil moisture estimation. We infer that using statistical estimation errors alone for comparison purposes may lead to erroneous conclusions regarding the advantages of one soil moisture inversion algorithm over another.     

Distribution of Errors in a Classified Map of Satellite Data

Abstract

The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty. The approach is based on evaluating the strength of class membership of pixels. This study is important as it can give an indication of the amount of error resulting from the mis‐classification of pixels of specific cover types as well as the spatial distribution of such pixels. The results show that the spatial distribution of erroneously classified pixels are not random and varies depending on the nature of cover types. The proportions of such pixels are higher in spectrally less clearly defined cover types such as grasslands.     

基于不同空间信息源的地物光谱反射率模拟图像分析

本文利用ERDAS Imaging软件中的Modeler模块,开发了地物光谱反射率图像的模拟技术。以野外实测地物光谱反射率数据为依据,用土地利用类型图和高分辨率遥感影像图作为地物空间分布的信息源,以陆地资源卫星TM1-4波段为例,模拟了4个波段的地物光谱反射率图像,合成了真彩色和标准假彩色图像。对基于不同空间信息源的地物光谱反射率模拟图像进行了对比分析,指出了进一步研究的方向。     

Archeological crop marks identified from Cosmo-SkyMed time series: the case of Han-Wei capital city,Luoyang, China

ABSTRACT

The development of spaceborne Synthetic Aperture Radar (SAR) technology declares that the golden era of SAR remote sensing in archeology is approaching; however, nowadays its methodology framework is still lacking due to the inadequate case studies validated by ground-truths. In this study, we investigated the crop marks using multi-temporal Cosmo-SkyMed data acquired in 2013 by applying a two-step decision-tree classifier in conjunction with a spatial analysis in an area of archeological interest nearby the archeological site of Han-Wei capital city (1900–1500 BP), in Luoyang, China. The time-series backscattering anomalies related to the wheat growth cycle were identified and then further validated in two zones by geophysical investigations (Ground Penetration Radar and electrical measurements) and in a third zone by archeological excavations made after the SAR data acquisition. This study provides a new approach for the relic detection, shallowly buried and covered by the crop vegetation, by temporal crop marks on spaceborne SAR images. We also emphasize the necessity to establish a satellite-to-ground methodology framework for the promotion of remote-sensing technology in archeology.     

一种光谱信息保持的影像融合算法研究

在介绍遥感图像融合中IHS变换和小波包分析的基础上,提出了一种基于光谱特征保持的IHS变换与小波包分析相结合的图像融合算法。该方法可以最大限度地保留待融合图像的光谱信息,同时融合图像的清晰度和空间分辨率有了很大提高,图像纹理信息也得到了很好的保持。通过对SAR图像与Landsat(TM)多光谱图像的融合实验,证明了该方法的有效性。     

基于图像特征的星载SAR图像模拟研究

SAR图像模拟技术被广泛应用于SAR系统的设计和验证、SAR图像的正射纠正、雷达图像解译和目标识别等。随着星载SAR的发展,必然面临着对星载SAR图像模拟的大量需求。本文首先从SAR图像的几何特征和辐射特征出发,探讨了SAR图像模拟技术的原理,分析了RD（Rang Doppler）模型,后向散射模型和斑噪模型。在传统RD模型的基础上,根据不同地形特征（起伏地形和平坦地形）考虑不同的后向散射模型。特别强调了在平坦地形情况下,需要地物分类数据的参与,并利用Ulaby和Dobson的后向散射模型。另外,在SAR图像统计特征的基础上,进行SAR图像的乘性噪声模拟,可以满足更逼真的SAR场景需求。然后,给出了图像模拟的算法流程,并对关键步骤的算法做了分析。最后,在实现基于图像特征的星载SAR图像场景模拟算法的基础上,选择新疆窝依牙地区和天津地区分别进行起伏地形和平坦地形的模拟试验,实验结果证明了本文模拟算法的有效性。     

内蒙古巴彦塔拉盆地构造与铀矿化的遥感地质研究

通过TM数据假彩色合成、纹理分析、目视解译及隐伏信息提取等手段,解译了内蒙古巴彦塔拉盆地内铀矿找矿目标层(K₁b)出露范围,解译并野外追索了NE向的控盆断裂组(F₁、F₂、F₃)、NNE向断裂组(F₂₀、F₂₁)以及NW、近EW向断裂组,通过对古河道、现代地貌水系的分析,总结了区内新构造运动的特点;通过热辐射信息、植被信息的提取,分析了地下水补-径-排的特点和铀矿化的关系。     

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT

Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.     

一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。