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1.
Vinay Kumar Prince Agrawal Shefali Agrawal 《Journal of the Indian Society of Remote Sensing》2017,45(3):407-416
Detailed and enhanced land use land cover (LULC) feature extraction is possible by merging the information extracted from two different sensors of different capability. In this study different pixel level image fusion algorithms (PCA, Brovey, Multiplicative, Wavelet and combination of PCA & IHS) are used for integrating the derived information like texture, roughness, polarization from microwave data and high spectral information from hyperspectral data. Span image which is total intensity image generated from Advanced Land observing Satellite-Phase array L-band SAR (ALOS-PALSAR) quad polarization data and EO-1 Hyperion data (242 spectral bands) were used for fusion. Overall PCA fused images had shown better result than other fusion techniques used in this study. However, Brovey fusion method was found good for differentiating urban features. Classification using support vector machines was conducted for classifying Hyperion, ALOS PALSAR and fused images. It was observed that overall classification accuracy and kappa coefficient with PCA fused images was relatively better than other fusion techniques as it was able to discriminate various LULC features more clearly. 相似文献
2.
《地理信息系统科学与遥感》2013,50(3):345-370
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods—principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)—were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%-5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%-6.1% and 7.6%-12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution. 相似文献
3.
Dengsheng Lu Guiying Li Emilio Moran Mateus Batistella Corina C. Freitas 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):798-808
This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method. 相似文献
4.
《International Journal of Digital Earth》2013,6(8):671-687
Remote-sensing data play an important role in extracting information with the help of various sensors having different spectral, spatial and temporal resolutions. Therefore, data fusion, which merges images of different spatial and spectral resolutions, plays an important role in information extraction. This research investigates quality-assessment methods of multisensor (synthetic aperture radar [SAR] and optical) data fusion. In the analysis, three SAR data-sets from different sensors (RADARSAT-1, ALOS-PALSAR and ENVISAT-ASAR) and optical data from SPOT-2 were used. Although the PALSAR and the RADARSAT-1 images have the same resolutions and polarisations, images are gathered in different frequencies (L and C bands, respectively). The ASAR sensor also has C-band radar, but with lower (25 m) resolution. Since the frequency is a key factor for penetration depth, it is thought that the use of different SAR data might give interesting results as an output. This study describes a comparative study of multisensor fusion methods, namely the intensity-hue-saturation, Ehlers, and Brovey techniques, by using different statistical analysis techniques, namely the bias of mean, correlation coefficient, standard deviation difference and universal image quality index methods. The results reveal that Ehlers' method is superior to the others in terms of spectral and statistical fidelity. 相似文献
5.
Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier. 相似文献
6.
高分二号影像融合方法质量评价 总被引：9，自引：0，他引：9
针对实际应用中因融合方法选择不恰当而无法充分发挥高分二号数据优势的问题,对常用影像融合算法进行了分析评价,旨在寻找最适用于高分二号数据的影像融合方法。采用Brovey变换、改进IHS变换、PCA变换、GS、HPF、小波PCA、Ehlers、Subtractive、HCS、NND 10种影像融合方法对高分二号影像进行融合处理,并从定性和定量两方面对融合结果进行质量评价。结果表明,针对高分二号数据,综合考虑光谱保真度和空间细节表达以及目视效果,HCS和NND方法的融合效果最佳。 相似文献
7.
选择适宜的融合方法有利于卫星遥感影像融合产品更好地服务于生产实践及科学研究。本文在总结现有影像像素级融合算法原理的基础上，选用Pansharp、Gram-Schmidt、HPF、Ehlers、Subtractive、Modified IHS、Brovey、PCA、NNDiffuse等多种常用的影像融合方法对国产高分影像的全色和多光谱数据进行了融合处理，并从定性和定量的角度对融合结果进行了详细评价，试图寻找适用于国产高分卫星遥感影像的最佳融合方法。结果表明：针对国产高分一号卫星遥感数据，超分辨率贝叶斯算法融合效果在视觉效果与影像质量定量评价指标中综合表现最佳；Gram-Schmidt、NNDiffuse、Subtractive和HPF融合结果地物边界最为清晰；Modified IHS、PCA、Brovey融合影像色彩失真较为明显；NNDiffuse在可见光波段表现较突出；Gram-Schmidt在近红外波段表现效果最佳。 相似文献
8.
Peijun Du Yu Chen Junshi Xia Kun Tan 《Journal of the Indian Society of Remote Sensing》2013,41(2):213-222
In this paper, we propose a novel scheme to improve the accuracy of remote sensing image classification by integrating data fusion, multiple feature combination and ensemble learning. Intensity-Hue-Saturation (IHS), Gram-Schmidt (GS), Brovey and wavelet fusion methods are first performed to obtain the optimal fusion images of high resolution and multispectral images. Support Vector Machine (SVM) classifier is then adopted to classify the fused image with different feature sets, and ensemble learning algorithm based on dynamic classifier selection (DCS) is finally used to integrate multiple classification maps. The proposed classification scheme is implemented with three remote sensing data sets, obtaining the highest overall accuracy and kappa coefficient in all cases (92.63% and 0.8917 for BJ-1 data set, 81.89% and 0.7513 for Landsat TM and SPOT4 data set, 92.21% and 0.8838 for ALOS data set respectively). The experimental results show that the integration of data fusion, feature combination and ensemble learning improves the classification performance obviously and has great potential in practical uses. 相似文献
9.
TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda. 相似文献
10.
多时相SAR变化检测技术,通过分析同一区域不同时相的SAR数据可以检测地表地物目标的变化信息,在土地资源调查及监测管理方面具有广泛应用。本文将长时间SAR图像相干性特征与目标幅度信息进行融合,并采用多时相SAR数据堆栈处理方法进行大区域城建目标分类及变化信息检测。最后采用13景ALOS PALSAR数据对中国天津地区2007~2010年期间的城建区域变化进行了检测实验,得到了良好的实验结果,并验证方法有效性。 相似文献
11.
The study examined the capability of dual-polarization SAR data for forest cover mapping and change assessment in the Brazilian Amazon Forest regions. Shuttle Imaging Radar (SIR)-C and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data were analysed to map and quantify deforestation. The images were classified using hybrid classifier, where each land cover was grouped in various spectral sub-classes interpreted on the imagery and later merged together to generate the desired land cover classes. The classification accuracy for forest was reasonably high (>90%). The technique applied in this study can be extended for operational mapping and monitoring of deforestation in the tropics, particularly for those regions which are often covered by cloud. 相似文献
12.
Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. 相似文献
13.
ALOS PALSAR双极化数据水稻制图 总被引：1，自引：0，他引：1
以江苏省海安县为研究区,使用2008年获取的日本ALOS卫星PALSAR双极化模式数据,分析水稻在L波段SAR图像上的后向散射特征,并提出相应的水稻制图方法。水稻在L波段上表现出了和C波段相同的时相变化特征。HH极化后向散射依赖于水稻植株的空间分布结构,某些机械插秧区域的布拉格共振现象引起水稻后向散射严重增强,给利用PALSAR数据水稻制图带来了困难。而HV极化不存在布拉格共振现象。在考虑布拉格共振影响的条件下,提出了联合PALSAR双极化模式HH和HV极化数据、基于时相变化特征进行水稻制图的方法,获得了88.4%的制图精度。 相似文献
14.
15.
一种基于小波系数特征的遥感图像融合算法 总被引：20，自引：2，他引：18
多光谱图像和全色图像是目前卫星遥感领域最常见的传感器图像.为了更充分地发挥这两类遥感图像数据的价值,人们利用两类数据的互补性,将多传感器融合技术引进了遥感图像处理领域.在IHS彩色空间变换和小波多分辨率分析的基础上,利用图像高频小波系数的多个特征来定义特征量积,并利用特征量积作为依据提出了一种图像融合新算法.通过一组多光谱图像和全色图像数据进行融合仿真试验,并将该算法与IHS,HPF等算法和归一化矩算法作了比较.证明该方法能在保留多光谱图像光谱信息的基础上,有效地提高多光谱图像的空间分辨率. 相似文献
16.
IHS变换和小波变换相结合的遥感影像融合 总被引：8，自引：0，他引：8
本文针对低分辨率多光谱影像与高分辨率全色影像的融合，提出了一种IHS变换和小波变换相结合的遥感影像融合方法。方法首先对多光谱影像作IHS正变换，得到亮度I、色度H和饱和度S三个分量：然后利用小波变换融合方法，融合多光谱影像的亮度分量与全色影像，并用融合后的影像替代多光谱影像的亮度分量；最后，利用IHS反变换得到新的多光谱影像。试验结果分析表明，新方法的性能优于IHS变换融合方法、小波变换融合方法，在增强融合影像的空间细节表现能力的同时，很好地保留了多光谱影像的光谱信息。 相似文献
17.
ALOS数据像素级融合方法比较研究 总被引：2，自引：0，他引：2
遥感数据融合是多源遥感海量数据富集表示的有效途径。如何在提高融合影像空间分辨率的同时最大限度地保持光谱信息是长期以来遥感数据融合研究的焦点内容。本文以ALOS PRISM和ALOS AVNIR-2传感器的数据为数据源,比较研究了遥感领域中常用和代表性的BROVEY、IHS、MULTIPLICATIVE、PCA、WAVELET和HPF六种融合方法,并通过主观评价和定量分析对融合效果进行了综合评价。实验结果表明,HPF方法在显著提高融合影像空间分辨率的同时,有效保持了多光谱影像的光谱信息,是适合ALOS数据的最优融合方法。 相似文献
18.
Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type. 相似文献
19.
Saygin Abdikan 《国际地球制图》2018,33(1):21-37
Remote sensing data utilize valuable information via various satellite sensors that have different specifications. Image fusion allows the user to combine different spatial and spectral resolutions to improve the information for purposes such as forest monitoring and land cover mapping. In this study, I assessed the contribution of dual-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar data to multispectral Landsat imagery. The research investigated the separability of forested areas using different image fusion techniques. Quality analysis of the fused images was conducted using qualitative and quantitative analyses. I applied the support vector machine image classification method for land cover mapping. Among all methods examined, the à trous wavelet transform method best differentiated the forested area with an overall accuracy (OA) of 94.316%, while Landsat had an OA of 92.626%. The findings of this study indicated that optical-SAR-fused images improve land cover classification, which results in higher quality forest inventory data and mapping. 相似文献
20.
Yangrong Ling Manfred Ehlers E. Lynn Usery Marguerite Madden 《ISPRS Journal of Photogrammetry and Remote Sensing》2007,61(6):381-392
Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. 相似文献