共查询到20条相似文献，搜索用时 15 毫秒
1.
S. Rajesh S. Arivazhagan K. Pratheep Moses R. Abisekaraj 《Journal of the Indian Society of Remote Sensing》2014,42(2):267-277
Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. But, it is difficult to classify satellite images since they include both pure pixels and boundary pixels. The boundary pixels are ‘mixed’ pixels, representing an area occupied by more than one ground cover. That is, class boundaries represented by pixels, are not sharp but fuzzy. This paper discuses the application of Adaptive Neuro-Fuzzy inference system (ANFIS) for classification of remotely sensed images that contains mixed pixels. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification. Genetic Algorithms (GA) based approach is analysed for the selection of a subset from the combination of Wavelet Packet Statistical Features (WPSF) and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the LISS IV images. GA has been employed to reduce the complexity and increase the accuracy of classification. Four indices—user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experiments show that the proposed approach produces better results compared to the results obtained when classical classifiers are used. 相似文献
2.
S. Rajesh S. Arivazhagan K. Pratheep Moses R. Abisekaraj 《Journal of the Indian Society of Remote Sensing》2013,41(2):237-248
A genetic algorithm based approach is used in this paper for the selection of a subset from the combination of Wavelet Packet Statistical and Wavelet Packet Co-occurrence textural feature sets to classify the LISS IV satellite images using neural networks. Generally, adding a new feature increases the complexity of training and classification. Hence there is a need to differentiate between those features that contribute ample information and others. Many current feature reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) involve linear transformations of the original pattern vectors to new vectors of lower dimensions. Hence a multi-objective Genetic Algorithm has been employed to reduce the complexity and increase the accuracy of classification. Four indices - user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experimental results show that the proposed Genetic Algorithm approach with lesser number of optimal features produces comparable results with that of our earlier approach using more features. 相似文献
3.
K. Venkateswaran N. Kasthuri N. Kousika 《Journal of the Indian Society of Remote Sensing》2017,45(6):903-911
Scalar wavelet based contourlet frame based features are used for improving the classification of remote sensing images. Multiwavelet an extension to scalar wavelets provides higher degree of freedom, which possess two or more scaling function and wavelet function. Unlike scalar wavelets, which has single scaling and wavelet function. Multiwavelet satisfies several mathematical properties simultaneously such as orthogonality, compact support, linear phase symmetry and higher order approximation. The multiwavelets considered here are Geronimo-Hardin-Massopust (GHM) and Chui Lian (CL). In this paper the performance of GHM and CL multiwavelet is compared. Finally CL based multicontourlet frame based features are used for improving the classification accuracy of remote sensing images as it has directional filter banks. Principal component analysis based feature reduction is performed and Gaussian Kernel Fuzzy C means classifiers are used to improve the classification accuracy. The experimental results shows that the CL based multicontourlet overall accuracy is improved to 5.3% (for LISS-IV(i)), 2.09% (for LISS IV(ii)) 4.17% (for LISS IV(iii)) and 4.2% (for LISS IV-(iv)) the kappa coefficient is improved to 0.04 (for LISS IV-(i)), 0.029 (for LISS IV-(ii)), 0.031 (for LISS IV-(iii)) and 0.05 (for LISS IV-(iv)) compared to Wavelet based Contourlet transform. 相似文献
4.
Image fusion techniques integrate complimentary information from multiple image sensor data such that the new images are more
suitable for the purpose of human visual perception and computer based processing tasks for extraction of detail information.
As an important part of image fusion algorithms, pixel-level image fusion can combine spectral information of coarse resolution
imagery with finer spatial resolution imagery. Ideally, the method used to merge data sets with high-spatial and highspectral
resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper describes the
Discrete Wavelet Transform (DWT) algorithm for the fusion of two images using different spectral transform methods and nearest
neighbor resampling techniques. This research paper investigates the performance of fused image with high spatial resolution
Cartosat-1(PAN) with LISS IV and Cartosat-1(PAN) sensor images with the LISS III sensor image of Indian Remote Sensing satellites.
The visual and statistical analysis of fused images has shown that the DWT method outperforms in terms of Geometric, Radiometric,
and Spectral fidelity. 相似文献
5.
介绍了基于小波包变换和区域方差的遥感影像融合方法.利用IHS变换和小渡包变换把全色影像和多光谱影像的相应分量分解为低频部分和高频部分,并分别采用加权平均法和区域方差法融合低频部分和高频部分,然后通过小波包重构和IHS逆变换得到最终的融合影像;最后采用MATLAB语言实现了这种方法.实验结果表明,这种方法在提高影像的清晰度、突出影像细节信息以及保留原始影像的光谱特征方面效果较好. 相似文献
6.
The supervised classification (Maximum likelihood) on three dates of IRS (LISS III) satellite data was performed to study
the effect of seasonal spectral variation on land cover classification for the study area falling in the Solan district of
Himachal Pradesh at latitude 30° 50’ N to 31° ’N and longitude 77° 00’ E to 77° 15’ E. It was found that the summer dataset
was better with overall classification accuracy of 76% as compared to winter and spring dataset with classification accuracy
of 49 and 46%, respectively. 相似文献
7.
R. Zoleikani M. J. Valadan Zoej M. Mokhtarzadeh 《Journal of the Indian Society of Remote Sensing》2017,45(1):25-33
In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and À Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %). 相似文献
8.
It has been always a challenging task to keep an ideal balance of spectral and spatial resolution for merging panchromatic image and multispectral image. The mathematical theories such as color space transformation and Wavelet Packet Analysis are usually employed in information fusion area. Combining color space conversion with wavelet packet theory is a way of researching remote sensing image fusion algorithms further. In the paper, there are three existing image fusion strategies applied to the second layer of frequency bands decomposed by wavelet packet analysis in the HSV and the IHS (triangular coordinate) color space, respectively. Serial experiments demonstrate two core concepts. One is the effects of image fusion strategies based on region is super to those of fusion strategy based on pixel for the same color space; the other is the different performances are measured in the two color spaces. Specially, the space definition for image fused in the former color space is inferior to that in the latter color space; while the spectrum content for image fused in the former color space retains better than in the latter color space, when using the same fusion strategy in the two color space. As a result, application containing HSV space conversion can alleviate spectral deterioration, whereas fusion operation of IHS transformation can lift spatial definition. 相似文献
9.
T T Medhavy Tara Sharma R P Dubey R S Hooda K E Mothikumar M Yadav M L Manchanda D S Ruhal A P Khera S D Jarwal 《Journal of the Indian Society of Remote Sensing》1993,21(1):21-28
The possibility of improving classification accuracies using different training strategies and data transformations within the framework of a supervised maximum likelihood classification scheme was explored in this study. The effect of spatial resolution of data on the accuracy of classification was also studied Single-pixel training strategy resulted in improved classification accuracy over the block-training method. Data transformations gave no significant improvements in accuracy over untransformed data. There was a reduction in classification accuracy as resolution of data improved from 72 m (LISS I) to 36 m (LISS II) while other sensor characteristics remained same. 相似文献
10.
The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two
different measures of accuracy, namely, producer’s accuracy (PA) and user’s accuracy (UA). The PA of a category indicates
to what extent the reference pixels of the category are correctly classified, whereas the UA of a category represents to what
extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories
determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA.
The present investigation has been performed in order to determine if there occurs improvement in the UA of the various categories
on the classified image of the principal components of the original bands and on the classified image of the stacked image
of two different years. We performed the analyses using the IRS LISS III images of two different years, i.e., 1996 and 2009,
that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area,
Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results
of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the
classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the
number of bands from two different years. On the other hand, the classified image of the principal components did not show
any improvement in the UA as compared to the original images. 相似文献
11.
《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
12.
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
13.
Spectral and Spatial Quality Analysis in Pan Sharpening Process 总被引：1，自引：0，他引：1
Image fusion is a process to obtain new images containing more information by combining images obtained same or different sensors. With most of the earth observation satellites, high spatial resolution panchromatic images and low spatial resolution multispectral images are obtained. As an example of image fusion ??pan sharpening?? is a process of combining of high spatial resolution panchromatic images and low spatial resolution multispectral images. At the end of the fusion process both high spatial and spectral resolution new images are obtained. In this study, panchromatic and multispectral images gathered from Ikonos were used. Panchromatic and multispectral images belonging to the same sensor were combined by using different image fusion methods. As pan sharpening methods Brovey transform, Modified IHS, Principal Component Analysis (PCA), Wavelet PC transform and Wavelet A Trous transformation methods were used. Quality of fused products was evaluated from the point of view of both visual and statistical criteria. While wavelet based methods are succesfull in terms of protection of spectral quality of original multispectral images, the colorbased and statistical methods are giving better results within the improvement of spatial content. 相似文献
14.
基于小波变换特征的遥感地貌影像纹理分析和分类 总被引：21，自引：1，他引：21
本文基于图像的正交小波变换特征，研究了遥感地貌纹理影像的特征提取和分类方法，并对２５幅地貌影像进行了分类。结果表明，所述方法不仅对同一分辨率的影像有较高的分类正确率，而且对不同分辨率的影像也有较高的分类正确率，同时对训练样本和考试样本来自不同母体的影像也有较高的分类正确率。 相似文献
15.
E. P. Nobi E. Dilipan K. Sivakumar T. Thangaradjou 《Journal of the Indian Society of Remote Sensing》2012,40(3):467-481
Seagrasses ecosystems are fragile yet highly productive ecosystems of the world showing declining trend throughout the world due to natural and anthropogenic pressures. Effective conservation and management plan is thus required to protect these resources, to aid with conservation need mapping and monitoring of seagrasses using high resolution remote sensing data is very much required. Hence, the present study was made to record the seagrass aerial cover in the Lakshadweep islands using IRS P6 LISS IV satellite data. The suitability of LISS IV sensor for seagrass mapping was tested for the first time with an overall accuracy of 73.16%. The study found an area of 2590.2?ha of seagrasses in Lakshadweep islands with 1310.8?ha and 1279.4?ha dense and sparse seagrass cover respectively. The study recommends the use of LISS IV data for mapping the shallow water seagrasses, as mapping efficiency increases nearly 4 times more than the LISS III data, as the former (LISS IV) picks up the small patches of seagrasses and delineates the coral and reef vegetation patches from seagrass class. 相似文献
16.
一种基于小波包变换的纹理图像压缩算法 总被引：1，自引：0，他引：1
提出一种基于小波包变换与自适应混合量化的新图像压缩算法.该算法首先用小波包变换对纹理丰富的图像进行完全分解,并用一种与后续编码器相关联的成本函数(CostFunction)进行最佳小波包基搜索;然后依据图像内容,自适应确定小波包系数的扫描次序;再对小波包变换后的最低频子带进行DPCM无失真编码,对高频子带实施矢量量化编码;最后对所形成的二进制符号流进一步实施自适应算术编码.仿真实验结果表明:提出的小波包图像压缩算法是一种比较好的编码方案,其压缩效果不仅明显优于JPEG算法与SPIHT算法(特别是纹理图像),而且优于已有的其他小波包图像压缩算法. 相似文献
17.
本论文尝试讨论两个主题：主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中，由于合成孔径雷达的数据具有全偏极特性，在此选取了对植被较为敏感的HV极化合成孔径雷达数据，与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先，本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声，在接下来融合步骤中，主成分分析出来的第一部分（PCI）是用做完滤除噪声后的合成孔径雷达取代，在数据融合后，进行地物分类是采用最大似然法来分类融合影像。在第二个主题中，利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性，其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声，接下来同样是使用采用最大似然法来分类融合影像，（不同的在于全偏极雷达影像使用Wishart几率分布，在光学影像采用multivariate Gaussian几率分布）将每个类别中每个像元属于某个类别的几率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果，也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。 相似文献
18.
K R Manjunath N Kundu S Panigrahy 《Journal of the Indian Society of Remote Sensing》1998,26(4):197-208
The present study evaluates the performance of Indian Remote Sensing (JRS) LISS Jl and LISS III data having spatial resolutions of 36 m and 23.5 m respectively in the Classification accuracy of rice, mustard and potato crops grown in West Bengal, India. The role of Middle infra-red (MIR.) band, of IRS 1C LISS III was also investigated in this context. The results indicated that in case of crop like rice which was grown over large contiguous fields, no significant change in classification accuracy was observed between LISS II and LISS III data. However, the accuracy increased by 5–7 per cent with the inclusion of MIR band mainly due to better separability between lowland rice and other hill vegetation. In case of crops like mustard and potato which were grown on small size or less contiguous fields, the classification accuracy increased by 5–8 per cent due to higher spatial resolution of LISS III. Inclusion of MIR band did not improve the accuracy of these crops. 相似文献
19.
20.
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