A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Comparison of pixel- and object-based classification in land cover change mapping

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel- and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995–2005. For single date thematic maps of 10 LULC classes, quantitative and visual analyses showed no significant accuracy difference between the two methods. The object-based method produced thematic maps with more uniform and meaningful LULC objects, but it suffered from absorption of small rare classes into larger objects and the incapability of spatial parameters (e.g. object shape) to contribute to class discrimination. Despite the similar map accuracies produced by the two methods, temporal change maps produced using post-classification comparison (PCC) and analysed using intensive visual analysis of errors of omission and commission revealed that the object-based maps depicted change more accurately than maximum likelihood classification (MLC)-derived change maps.     

Monitoring changes in land use land cover of Yamuna riverbed in Delhi: a multi-temporal analysis

Impact of anthropogenic activities has led to significant changes in riverbeds over a period of time. The objective of the study was to monitor the land use land cover (LULC) of Yamuna riverbed in Delhi and to assess the changes due to natural and anthropogenic activities. The maximum likelihood classification was carried out by using March 1977, April 1999, April 2002 and February 2009 imageries. An overall accuracy of LULC classification of 2009 imagery was around 88.6% based on ground truth data. A significant reduction in the main river course and increase in agricultural activities as well as built up were observed in remote sensing analysis as an outcome of riverbed encroachments. The changed scenario of riverbed not only stresses the riverine ecosystem but also jeopardizes the future consequences on river hydraulics.     

Urban expansion modeling using an enhanced decision tree algorithm

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

     

Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape

From its inception, land-use and land-cover mapping have been major themes in remote-sensing research and applications. Although frequently considered together, land use and land cover (LULC) are defined differently, with land use referring to the economic function of the Earth’s surface and land cover to its natural or engineered biophysical cover. Land cover can be observed directly using remote sensing, but land use must be inferred from the cover type. In this study, we test whether object-based image analysis (OBIA) can improve the land-cover and land-use classification in a complex agricultural landscape located along the border between Poland and Ukraine. We quantitatively compared the results of OBIA-based versus per-pixel classifications for both land cover and land use, respectively. Our results show that land-cover classification was not significantly improved when OBIA-based methods were used. Although overall classification accuracy was modest, land-use classification was significantly improved when OBIA-based methods were applied using both spectral and spatial/geometric features of image objects, but not when spectral or spatial/geometric features were used independently. Our results suggest that in anthropogenically altered landscapes where the geometry and arrangement of surface spatial structure may convey land-use information, use of OBIA-based techniques may provide a powerful tool for improving classification.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing

Mixture analysis is a necessary component for capturing sub-pixel heterogeneity in the characterization of land cover from remotely sensed images. Mixture analysis approaches in remote sensing vary from conventional linear mixture models to nonlinear neural network mixture models. Linear mixture models are fairly simple and generally result in poor mixture analysis accuracy. Neural network models can achieve much higher accuracy, but typically lack interpretability. In this paper we present a mixture discriminant analysis (MDA) model for inferring land cover fractions within forest stands from Landsat Thematic Mapper images. Specifically, individual class distributions are modeled as mixtures of subclasses of Gaussian distributions, and land cover fractions are estimated using the corresponding posterior probabilities. Compared to a benchmark study on accuracy of mixture models with Plumas National Forest data, this MDA model easily outperforms traditional linear mixture models and parallels the performance of the ARTMAP neural network mixture model. In other words, the MDA model is observed to successfully combine the performance characteristics of more complex neural network models (due to the nonlinear nature of its classification rules), with the ease of interpretation associated with linear mixture models (due to its relatively simple structure). MDA models therefore offer an attractive alternative for addressing the mixture modeling problem in remote sensing.     

Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America

While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP) - a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation - has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.     

Optimization at multi-spectral land cover classification with fuzzy clustering and the Kohonen feature map

The number and structure of land cover classes separatable in a region on the basis of multi-spectral satellite images are usually known. The method presented here is capable of combining information from the spectral and nonspectral channels of multi-spectral images in a way that offers a possibility to cover all spectrally separable classes. The method also enables the determination of the representativeness of the available training areas with respect to these classes. In the course of the procedure, 5-35 classes are iteratively determined in succession for different channel combinations with the Kohonen feature map and the fuzzy c-means clustering algorithm. The maximum Jeffries-Matusita (JM) distance between these classes indicates the optimum class number of unambiguously separable classes for the entire multi-spectral image. In a simple step, the available training areas are grouped by the fuzzy c-means clustering algorithm. In this case, the maximum JM distance indicates the class number that ensures optimum separation of training areas. If this class number is smaller than the optimum class number determined for the entire scene, then further training areas must be defined in order to improve the results of a subsequent supervised land cover classification. The method's efficiency is demonstrated by the example of land cover classification for the region around Perpignan.     

A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem. Using IKONOS high spatial resolution imagery, we examined whether the IO technique could significantly improve classification accuracy compared to the pixel-based method when applied to urban land cover mapping in Tampa Bay, FL, USA. We further compared the performance of an artificial neural network (ANN) and a minimum distance classifier (MDC) in urban detailed land cover classification and evaluated whether the classification accuracy was affected by the number of extracted IO features. Our analysis methods included IKONOS image data calibration, data fusion with the pansharpening (PS) process, Hue–Intensity–Saturation (HIS) transferred indices and textural feature extraction, and feature selection using a stepwise discriminant analysis (SDA). The classification results were evaluated with visually interpreted data from high-resolution (0.3 m) digital aerial photographs. Our results indicate a statistically significant difference in classification accuracy between pixel- and object-based techniques; ANN outperforms MDC as an object-based classifier; and the use of more features (27 vs. 9 features) increases the IO classification accuracy, although the increase is statistically significant for the MDC but not for the ANN.     

Hierarchical Land Cover Classification based on MODIS NDVI Time-series

In this paper,we mainly used MODIS NDVI time-series dataset at 16-days temporal resolution and 250-meters spatial resolution to analyze land cover mapping of northeastern China.We used two different filter methods to fit NDVI time-series dataset,and compared their average classes’ separability based on Jeffries-Matusita distance index.In addition,we made use of hierarchical classification method to complete classification,combined with short-wave infrared spectral reflectance data and DEM.We conformed to the principle that separate area hierarchically into several parts first and then classify each part further,and use a single characteristic band first and then multiple feature bands.In the process of classification,we adopted threshold value method,support vector machine,artificial net neural and C5.0 decision tree classification to distinguish each land-cover type hierarchically.Finally,we evaluated the accuracy of the final classification of study area using known land-cover classification data and high-resolution remote sensing imagery,overall accuracy is 84.61%,Kappa coefficient is 0.8262.     

Discriminating native and plantation forests in a Landsat time-series for land use policy design

The Warren River Catchment of south-western Australia is an area of high biodiversity threatened by the loss of native vegetation and dryland salinity. Over the last 20 years, it has been the target of a series of policies that encourage conversion of agricultural land to plantation forest. Remote sensing has a key role in measuring trends in the area of plantation forest observed across the landscape and hence the effectiveness of policy initiatives. Despite its importance to land use policy, accurate data on historical land use and land cover (LULC) dynamics of two spectrally similar but ecologically distinct forest types – such as native forest and plantation forest – are not readily available for south-western Australia, largely due to prohibitive data delivery costs. However, we argue that regular low-cost monitoring of long-term change in the spatial distribution of plantation forest through remote sensing is a critical input into environmental policy for the catchment. To this end, a 35-year time-series of Landsat imagery was acquired, and three different classifiers were tested (Support Vector Machines – SVM; Random Forests – RF; and Classification and Regression Trees – CART) on spectral and textural indices applied to four spectral bands. The six major LULC classes considered were agriculture, water, native forest, sand dunes, plantation forest and harvested native forest. In classifying the imagery the SVM and RF outperformed the CART across all classes. However, the SVM classifier gave a slightly higher F-score for most individual classes than the RF. Eucalypt dominated plantation forest reaching full canopy cover was subject to the highest rates of misclassification inasmuch as it shares spectral properties with the Eucalypt dominant native forest. When applied to Landsat time-series imagery, SVM classifier combined with four bands held in common between the four Landsat sensors, and derived textures metrics are valuable in classifying plantation and native forest, particularly where these have a similar species composition. The differences in prediction accuracy when including additional Landsat bands were not statistically significant, as demonstrated by the McNemar test. Thus, we achieved a trade-off in reducing processing time without significantly impacting on classification accuracy (≥86%). The relatively high accuracy of the proposed method enables the effects of past policy initiatives to be observed, and hence the efficient design of environmental and conservation policy in the future.     

Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network

Mixed pixels are a major problem in mapping land cover from remotely sensed imagery. Unfortunately, such imagery may be dominated by mixed pixels, and the conventional hard image classification techniques used in mapping applications are unable to appropriately represent the land cover of mixed pixels. Fuzzy classification techniques can, however, accommodate the partial and multiple class membership of mixed pixels, and be used to derive an appropriate land cover representation. This is, however, only a partial solution to the mixed pixel problem in supervised image classification. It must be reognised that the land cover on the ground is fuzzy, at the scale of the pixel, and so it is inappropriate to use procedures designed for hard data in the training and testing stages of the classification. Here an approach for land cover classification in which fuzziness is accommodated in all three stages of a supervised classification is presented. Attention focuses on the classification of airborne thematic mapper data with an artificial neural network. Mixed pixels could be accommodated in training the artificial neural network, since the desired output for each training pixel can be specified. A fuzzy land cover representation was derived by outputting the activation level of the network's output units. The activation level of each output unit was significantly correlated with the proportion of the area represented by a pixel which was covered with the class associated with the unit (r>0.88, significant at the 99% level of confidence). Finally, the distance between the fuzzy land cover classification derived from the artificial neural network and the fuzzy ground data was used to illustrate the accuracy of the land cover representation derived. The dangers of hardening the classification output and ground data sets to enable a conventional assessment of classification accuracy are also illustrated; the hardened data sets were over three times more distant from each other than the fuzzy data sets.     

Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier

Several investigations indicate that the Bidirectional Reflectance Distribution Function (BRDF) contains information that can be used to complement spectral information for improved land cover classification accuracies. Prior studies on the addition of BRDF information to improve land cover classifications have been conducted primarily at local or regional scales. Thus, the potential benefits of adding BRDF information to improve global to continental scale land cover classification have not yet been explored. Here we examine the impact of multidirectional global scale data from the first Polarization and Directionality of Earth Reflectances (POLDER) spacecraft instrument flown on the Advanced Earth Observing Satellite (ADEOS-1) platform on overall classification accuracy and per-class accuracies for 15 land cover categories specified by the International Geosphere Biosphere Programme (IGBP).

A set of 36,648 global training pixels (7 × 6 km spatial resolution) was used with a decision tree classifier to evaluate the performance of classifying POLDER data with and without the inclusion of BRDF information. BRDF ‘metrics’ for the eight-month POLDER on ADEOS-1 archive (10/1996–06/1997) were developed that describe the temporal evolution of the BRDF as captured by a semi-empirical BRDF model. The concept of BRDF ‘feature space’ is introduced and used to explore and exploit the bidirectional information content. The C5.0 decision tree classifier was applied with a boosting option, with the temporal metrics for spectral albedo as input for a first test, and with spectral albedo and BRDF metrics for a second test. Results were evaluated against 20 random subsets of the training data.

Examination of the BRDF feature space indicates that coarse scale BRDF coefficients from POLDER provide information on land cover that is different from the spectral and temporal information of the imagery. The contribution of BRDF information to reducing classification errors is also demonstrated: the addition of BRDF metrics reduces the mean, overall classification error rates by 3.15% (from 18.1% to 14.95% error) with larger improvements for producer's accuracies of individual classes such as Grasslands (+ 8.71%), Urban areas (+ 8.02%), and Wetlands (+ 7.82%). User's accuracies for the Urban (+ 7.42%) and Evergreen Broadleaf Forest (+ 6.70%) classes are also increased. The methodology and results are widely applicable to current multidirectional satellite data from the Multi-angle Imaging Spectroradiometer (MISR), and to the next generation of POLDER-like multi-directional instruments.     

Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from Advanced Very High Resolution Radiometer (AVHRR) data with 1 km spatial resolution using a six-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat Thematic Mapper (TM) and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FPAR maps. Further, the main source of disagreement in LAI and FPAR maps can be attributed to specific biome classes that are characterized by a continuum of fractional cover and canopy structure.     

LM-BP神经网络在遥感影像分类中的应用研究

有效地利用卫星遥感数据进行多类别识别并提高分类精度一直是遥感应用研究的前沿。以江苏南京江宁区为试验区,复合最佳指数提取的波段组合光谱信息、灰度共生矩阵提取的纹理信息和地理辅助数据及其派生信息,运用LM-BP神经网络实现遥感影像分类,并将分类结果与标准BP网络和传统分类方法进行了比较。研究表明,将卫星数据与地理辅助数据结合,发展多源多维信息复合的LM-BP方法可以大大提高分类的精度,是提高遥感应用性的有效途径。     

Extraction of training samples from time-series MODIS imagery and its utility for land cover classification

A number of classification techniques to generate land cover maps from satellite imagery have been proposed but supervised classification with manual selection and delineation of training samples (TSs) continues to be the preferred technique. The current practices of field visits and manual delineation of TSs by visual recognition are highly demanding on both resources and time, with limited utility. With an increase in the number of Earth Observation Satellite (EOS) platforms and the enormous data that they generate, there is a need to process the data quickly and efficiently for creating global science products. Towards this goal, an attempt has been made in this article to develop a method for the automatic extraction of the TSs from the time series of Moderate Resolution Imaging Spectroradiometer (MODIS – 250 m) vegetation index (VI), which can then be used for supervised classification to create a land cover map with any classification technique on relevant remotely sensed data. The TSs contained 1.27%, 0.09% and 1.18% of the total pixels for the forest, crop and water classes of the study region. Validation with Advanced Wide Field Sensor (AWiFS – 56 m)-derived national land use/land cover (LULC) map of India shows a complete agreement with the location of what can be considered as pure class pixels. The article also demonstrates and compares the utility of these TSs with an expert choice of TSs on MODIS time-series data using k-nearest neighbour, and support vector machine (SVM) classifiers and on a single-scene Linear Imaging Self-Scanning Sensor-3 (LISS-3 – 24 m) imagery using maximum likelihood (ML) classifier.     

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.