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1.
The mixed pixels are treated as noise or uncertainty in class allocation of a pixel and conventional hard classification algorithms
may thus produce inaccurate classification outputs. Thus application of sub-pixel or soft classification methods have been
adopted for classification of images acquired in complex and uncertain environment. The main objective of this research work
has been to study the effect of feature dimensionality using statistical learning classifier — support vector machine (SVM
with sigmoid kernel) while using different single and composite operators in fuzzy-based error matrixes generation. In this
work mixed pixels have been used at allocation and testing stages and sub-pixel classification outputs have been evaluated
using fuzzy-based error matrixes applying single and composite operators for generating matrix. As subpixel accuracy assessment
were not available in commercial software, so in-house SMIC (Sub-pixel Multispectral Image Classifier) package has been used.
Data used for this research work was from HySI sensor at 506 m spatial resolution from Indian Mini Satellite-1 (IMS-1) satellite
launched on April 28, 2008 by Indian Space Research Organisation using Polar Satellite Launch Vehicle (PSLV) C9, acquired
on 18th May 2008 for classification output and IRS-P6, AWIFS data for testing at sub-pixel reference data. The finding of this research
illustrate that the uncertainty estimation at accuracy assessment stage can be carried while using single and composite operators
and overall maximum accuracy was achieved while using 40 (13 to 52 bands) band data of HySI (IMS-1). 相似文献
2.
Akshara Preethy Byju Anil Kumar Alfred Stein A. Senthil Kumar 《Journal of the Indian Society of Remote Sensing》2018,46(9):1519-1526
This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included. 相似文献
3.
In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%. 相似文献
4.
Ch. Venkateswara Rao K.M.M. Rao P. Shasidhar Reddy Girish Pujar 《International Journal of Applied Earth Observation and Geoinformation》2008,10(2):165-1
The Resourcesat-I satellite is equipped with different types of sensors with varied characteristics. For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique. Image fusion enhances the information content and helps in better discrimination of various land cover types. The Resourcesat-1 has equipped with three sensors, AWiFS, LISS-III and LISS-IV, which are having identical spectral resolutions, with different spatial, radiometric and temporal resolutions. The spatial resolutions ratio of the data set for merging are required to be maximum of 1:6, where as the data sets (AWiFS and LISS-III) that are used in the current study are having the ratio of 1:2.5 approximately. A novel merging technique is designed, which retains the multi-spectral response of the input data in the output data. The merged data set provides the higher spatial and radiometric resolutions. In order to evaluate the fusion merits quantitatively, all the data sets are digitally classified and studied the output classes for homogeneity and clear discrimination. A comprehensive comparative study is carried out between the fused image and the LISS-III image based on the contingency matrix and the scatter plots, which demonstrates the strength of fused image for discriminating the object classes at 23.5 m spatial and 10-bit radiometric resolutions. The merged data set gives the improved classification accuracy. 相似文献
5.
Vijaya Musande Anil Kumar Karbhari Kale 《Journal of the Indian Society of Remote Sensing》2012,40(4):589-597
Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others. 相似文献
6.
Yong Ge Sanping Li V. Chris Lakhan Arko Lucieer 《International Journal of Applied Earth Observation and Geoinformation》2009
The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels. 相似文献
7.
M.V.R. Sesha Sai P.V. Narasimha Rao 《International Journal of Applied Earth Observation and Geoinformation》2008,10(2):206-1
Subsequent to the launch of the state-of-art third generation Indian Remote Sensing satellite, Resourcesat-1, studies have been conducted to understand the capabilities of the on-board sensors for crop discrimination. The paper discusses the unique capabilities of the AWiFS, LISS-III and LISS-IV sensors in terms of their dimensionality, radiometry and spatial resolutions for crop discrimination and monitoring. The studies have indicated better crop discriminability especially using the short wave infrared data in 1.55–1.70 μm data among the spectrally confusing land cover classes, attributed to the relative differences of water contents. 10-bit radiometry of AWiFS data in four bands has been observed to be a better discriminant. Intrafield variability was very well captured by the LISS-IV data revealing the potential of data for applications like precision farming. The studies have revealed that potential of Resourcesat-1 data becoming the workhorse for several agricultural applications. 相似文献
8.
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10.
Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach 总被引：1，自引：0，他引：1
In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104 for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely. 相似文献
11.
G. Misra A. Kumar N. R. Patel R. Zurita-Milla 《Journal of the Indian Society of Remote Sensing》2014,42(2):325-334
Mapping a specific crop using single date multi-spectral imagery remains a challenging task because vegetation spectral responses are considerably similar. The use of multi-temporal images helps to discriminate specific crops as the classifier can make use of the uniqueness in the temporal evolution of the spectral responses of the different vegetated classes. However, one major concern in multi-temporal studies is the selection of optimum dates for the discrimination of crops as the use of all available temporal dates can be counterproductive. In this study this concern was addressed by selecting the best 2, 3, 4… combinations dates. This was done by conducting a separability analysis between the spectral response of the class of interest (here, sugarcane-ratoon) and non-interest classes. For this analysis, we used time series LISS-III and AWiFS sensors data that were classified using Possibilistic c-Means (PCM). This fuzzy classifier can extract single class sub-pixel information. The end result of this study was the detection of best (optimum) temporal dates for discriminating a specific crop, sugarcane-ratoon. An accuracy of 92.8 % was achieved for extracting ratoon crop using AWiFS data whereas the optimum temporal LISS-III data provided a least entropy of 0.437. Such information can be used by agricultural department in selecting an optimum number of strategically placed temporal images in the crop growing season for discriminating the specific crop accurately. 相似文献
12.
N. Rama Rao P. K. Garg S. K. Ghosh 《Journal of the Indian Society of Remote Sensing》2006,34(1):69-78
Motivated by the increasingly availability and importance of hyperspectral remote sensing data, this study aims to determine
whether current generation narrowband hyperspectral remote sensing data could be used to estimate vegetation Leaf Area Index
(LAI) accurately than the traditional broadband multispectral data. A comparative study has been carried out to evaluate the
performance of the narrowband Normalized Difference Vegetation Index (NDV1) derived from Hyperion hyperspectral sensor with
that of derived from IRS LISS-III for the estimation of LAI of some major agricultural crops (e.g. cotton, sugarcane and rice)
in part of Guntur district, India. It has been found that the narrowband NDVI derived from Hyperion has shown better results
over its counterpart derived from broadband LISS-III. Linear regression models have been used which with selected subsets
of individual Hyperion bands performed better to predict LAI than those based on the broadband datasets, although the potential
to overfit models using the large number of available Hyperion bands is a concern for further research. 相似文献
13.
The present study is based on digital analysis of IRS 1C/1D LISS-III and IRS P6 LISS-IV images for identification of lineaments in Meja Thermal Power Plant site in Allahabad district. In spite of all limitations due to anthropogenic activities of stone quarrying and criss cross unmetalled roads, almost non-existent vegetation indicators and soil moisture, the efforts were made to identify and map a number of lineaments in the study area and these were subsequently verified in the field. Most of the lineaments identified and mapped are not having any major displacement along them or are not displacing or truncating any major rock unit. Field investigations have revealed that there is no drastic change in the trend of rocks along the lineaments mapped in the study area. On the basis of digital analysis of IRS P6 LISS-IV data NE–SW and NW–SE trending lineaments have been identified and mapped. Some NNE–SSW trending lineaments have also been mapped. All the lineaments demarcated in this area still need to be correlated with the seismic data of the area by superimposing the microseismicity data over lineaments. 相似文献
14.
通过训练样本采样处理改善小宗作物遥感识别精度 总被引：1，自引：0，他引：1
训练样本质量是决定农作物遥感识别精度的关键因素,虽然高空间分辨率卫星的发展有效地解决了农作物遥感识别过程中的混合像元问题,但是当区域内不同作物种植面积差异较大时,训练集中不同类别样本数量往往相差较大,这样的不均衡数据集影响分类器的训练,导致少数类别的识别精度不理想。为研究作物遥感识别过程中的不均衡样本问题,本文基于GF-2号卫星数据,首先挖掘了地物的光谱信息、纹理信息,用特征递归消除RFE (Recursive Feature Elimination)方法进行特征优选,然后从数据处理的角度采用了5种采样算法对不均衡训练集进行处理,最后使用采样后的均衡数据集训练分类器,对比数据采样前后决策树与Adaboost(Adaptive Boosting)两种分类器的识别结果,发现:(1)经过采样处理后两种分类算法明显提升了小宗作物的分类精度;(2)经过ADASYS (Adaptive synthetic sampling)采样处理后,分类器性能提升最多,决策树的Kappa系数提高了14.32%,Adaboost的Kappa系数提高了10.23%,达到最高值0.9336;(3)过采样的处理效果优于欠采样,过采样对分类器的性能提升更多。综上所述,选择合适的采样方法和分类方法是提高不均衡数据集遥感分类精度的有效途径。 相似文献
15.
R. S. Tiwari Manoj K. Arora T. Kailash 《Journal of the Indian Society of Remote Sensing》1999,27(4):225-234
With increasing resolution of the remotely sensed data the problems of images contaminated by mixed pixels arc frequent. Conventional
classification techniques often produce erroneous results when applied to images dominated by mixed pixels. This may load
to unrealistic representation of land cover, thereby, affecting efficient planning, management and monitoring of natural resources.
Consequently, soft classification techniques providing sub-pixel land cover information may have to be utilised.
From a range of soft classification techniques, the present study focuses on the utility of conventional maximum likelihood
classifier and linear mixture modelling for sub-pixel. land cover classifications. The accuracy of the soft classifications
has been assessed using distance measures and correlation co-efficient. The results show that linear mixture modelling has
produced accuracies comparable to maximum likelihood classifier. Besides this the correlations between actual land cover proportions
and proportions from linear mixture modelling, though not strong, arc statistically significant at 95% level of confidence.
It has also been observed that the normalised likelihoods of maximum likelihood classifier also show strong correlations with
the actual land cover proportions on ground and therefore has the potential to be used as a soft classification technique. 相似文献
16.
Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks. 相似文献
17.
Crop mapping is one major component of agricultural resource monitoring using remote sensing. Yield or water demand modeling requires that both, the total surface that is cultivated and the accurate distribution of crops, respectively is known. Map quality is crucial and influences the model outputs. Although the use of multi-spectral time series data in crop mapping has been acknowledged, the potentially high dimensionality of the input data remains an issue. In this study Support Vector Machines (SVM) are used for crop classification in irrigated landscapes at the object-level. Input to the classifications is 71 multi-seasonal spectral and geostatistical features computed from RapidEye time series. The random forest (RF) feature importance score was used to select a subset of features that achieved optimal accuracies. The relationship between the hard result accuracy and the soft output from the SVM is investigated by employing two measures of uncertainty, the maximum a posteriori probability and the alpha quadratic entropy. Specifically the effect of feature selection on map uncertainty is investigated by looking at the soft outputs of the SVM, in addition to classical accuracy metrics. Overall the SVMs applied to the reduced feature subspaces that were composed of the most informative multi-seasonal features led to a clear increase in classification accuracy up to 4.3%, and to a significant decline in thematic uncertainty. SVM was shown to be affected by feature space size and could benefit from RF-based feature selection. Uncertainty measures from SVM are an informative source of information on the spatial distribution of error in the crop maps. 相似文献
18.
Li LuoGiorgos Mountrakis 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):579-587
A classification model was demonstrated that explored spectral and spatial contextual information from previously classified neighbors to improve classification of remaining unclassified pixels. The classification was composed by two major steps, the a priori and the a posteriori classifications. The a priori algorithm classified the less difficult image portion. The a posteriori classifier operated on the more challenging image parts and strived to enhance accuracy by converting classified information from the a priori process into specific knowledge. The novelty of this work relies on the substitution of image-wide information with local spectral representations and spatial correlations, in essence classifying each pixel using exclusively neighboring behavior. Furthermore, the a posteriori classifier is a simple and intuitive algorithm, adjusted to perform in a localized setting for the task requirements. A 2001 and a 2006 Landsat scene from Central New York were used to assess the performance on an impervious classification task. The proposed method was compared with a back propagation neural network. Kappa statistic values in the corresponding applicable datasets increased from 18.67 to 24.05 for the 2006 scene, and from 22.92 to 35.76 for the 2001 scene classification, mostly correcting misclassifications between impervious and soil pixels. This finding suggests that simple classifiers have the ability to surpass complex classifiers through incorporation of partial results and an elegant multi-process framework. 相似文献
19.
The 8 October 2005 earthquake caused widespread destruction in both the state of Jammu and Kashmir of India and Northern Pakistan. Due to poor accessibility in the hazardous and difficult mountainous terrain, a proper and comprehensive ground-based survey was not possible. However, with the help of remote sensing data and its analysis techniques, it is feasible to undertake both earthquake-related damage identification and assessment. This study attempts to document and identify built-up damaged (BD) areas using spectral indices taking temporal multispectral images from IRS-P6 LISS-IV. Five spectral indices have been used to identify BD areas using supervised possibilistic c-means (PCM) and noise cluster (NC) classifiers, to analyse the satellite data. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices provide the best results for identifying BD areas, while Simple Ratio (SR) index gives the best results for built-up undamaged area identification. Further, it observed that PCM classifier performed better in comparison to NC classifier. 相似文献
20.
Sreenivas Kandrika P.S. Roy 《International Journal of Applied Earth Observation and Geoinformation》2008,10(2):186-1
An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy. 相似文献