Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing supervised or unsupervised classification methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA); (2) development of LULC maps with assistance of reference data; and (3) accuracy assessments of all the LULC maps using independent reference data and selection of one LULC map with the highest accuracy. Classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences.     

Land cover update by supervised classification of segmented ASTER images

The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multi‐spectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52–2.43?µm. The image was initially ortho‐rectified and segmented into 51?186 objects, with an average object size of 135?pixels (about 3?ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K‐NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6% for the fuzzy classifier, 70.5% for the SVM, 60.9% for the K‐NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi‐spectral space defined by the nine ASTER bands used.     

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.     

Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery

Classifying the pixels of satellite images into homogeneous regions is a very challenging task as different regions have different types of land covers. Some land covers contain more regions, while some contain relatively smaller regions (e.g., bridges, roads). In satellite image segmentation, no prior information is available about the number of clusters. Here, in this paper, we have solved this problem using the concepts of semi-supervised clustering which utilizes the property of unsupervised and supervised classification. Three cluster validity indices are utilized, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. The first two cluster validity indices, symmetry distance based Sym-index, and Euclidean distance based I-index, are based on unsupervised properties. The last one is a supervised information based cluster validity index, Minkowski index. For supervised information, initially fuzzy C-mean clustering technique is used. Thereafter, based on the highest membership values of the data points to their respective clusters, randomly 10 % data points with their class labels are chosen. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on three satellite image data sets of different cities of India. Results are also compared with existing clustering techniques.     

A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data

Classification of remotely sensed data involves a set of generalization processes, i.e. reality is simplified to a set of a few classes that are relevant to the application under consideration. This article introduces an approach to image classification that uses a class hierarchy structure for mapping unit definition at different generalization levels. This structure is implemented as an operational relational database and allows querying of more detailed land cover/use information from a higher abstraction level, which is that viewed by the map user. Elementary mapping units are defined on the basis of an unsupervised classification process in order to determine the land cover/use classes registered in the remotely sensed data. Mapping unit composition at different generalization levels is defined on the basis of membership values of sampled pixels to land cover/use classes. Unlike fuzzy classifications, membership values are presented to the user at mapping unit level.     

Impervious surface mapping with Quickbird imagery

This research selects two study areas with different urban developments, sizes and spatial patterns to explore suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of ‘salt-and-pepper’ pixels, and segmentation-based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. To accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance.     

Comparison of fuzzy c-means classification,linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels

Three different 'soft' classifiers (fuzzy c-means classifier, linear mixture model, and probability values from a maximum likelihood classification) were used for unmixing of coarse pixel signatures to identify four land cover classes (i.e., supervised classifications). The coarse images were generated from a 30m Thematic Mapper (TM) image; one set by mean filtering, and another using an asymmetric filter kernel to simulate Multi-Spectral Scanner (MSS) sensor sampling. These filters collapsed together windows of up to 11 11 pixels. The fractional maps generated by the three classifiers were compared to truth maps at the corresponding scales, and to the results of a hard maximum likelihood classification. Overall, the fuzzy c-means classifier gave the best predictions of sub-pixel landcover areas, followed by the linear mixture model. The probabilities differed little from the hard classification, suggesting that the clusters should be modelled more loosely. This paper demonstrates successful methods for use and comparison of the classifiers that should ideally be extended to a real dataset.     

Impervious surface mapping with Quickbird imagery

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of "salt-and-pepper" pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance.     

Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system

Abstract

Information on actual land cover is necessary for various applications, such as soil and groundwater protection studies and hydrological studies. Therefore, a decision to produce a national land cover data base of the Netherlands, using satellite images, was made. The first version of the data base is now available for the whole of the Netherlands. Prior to the supervised classification the area was stratified in more or less homogeneous areas. Because cost, time and logistics required for a random sampling of the entire country were prohibitive, a mixed quantitative/qualitative classification accuracy assessment procedure was proposed. Classification performances were quantitatively assessed by comparing the classification results with digitized ground reference maps using a Geographical Information System (GIS). This offers a flexible method for locating the incorrectly labelled pixels and determining the possible reasons thereof. Classification accuracy of the land cover types which do not change much in time was also qualitatively assessed, using aerial photographs and topographical maps. The land cover data derived from remote sensing images can be readily combined with other digitized geographical data bases (e.g. soil maps).

The results of the proposed classification and validation procedure are presented for a test site situated in a stratum in the south of the Netherlands. It is shown how the land cover data are applied in a soil and groundwater vulnerability assessment system.     

Color clustering and learning for image segmentation based on neural networks

An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L/sup */u/sup */v/sup */ color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.     

Fuzzy function approximation with ellipsoidal rules

A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case.     

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS

The land use/cover distribution on Langkawi Island, Malaysia was mapped using remote sensing and a Geographic Information System (GIS). A Landsat Thematic Mapper (TM) satellite image taken in March 1995 was processed, geocorrected and analysed using IDRISI, raster-based GIS software. An unsupervised classification was performed based on spectral data from a composite image of the bands TM3, TM4 and TM5. Using this output, field data together with available secondary data consisting of topography, land use and soil maps were used to perform a maximum likelihood supervised classification. The overall accuracy of the output image was 90% and individual class accuracy ranged from 74% for rubber to 100% for paddy fields. The classified areas on the image were mainly confined to the mountainous and hilly regions on the island. A shaded relief map, simulating sunshine conditions, showed that the unclassified areas are located in the shadowed slopes, i.e. the slopes facing west. Consequently, the imagery was subdivided on the basis of slope aspect and a stratified classification was performed. As a result of this procedure, the overall accuracy increased to 92% and the individual class accuracy for the inland forest class increased by 9% to 90% . Using IDRISI, individual class areas as well as percentages were calculated. The kappa coefficient for the classified image was 0.90. Qualitative analysis indicates that topography is the main control on the spatial distribution of land use/cover types on the island. As Langkawi Island has been developing rapidly over the last decade, successful planning will require reliable information about land use/cover distribution and change. This study illustrates that remote sensing and GIS techniques are capable of providing such information.     

General fuzzy min-max neural network for clustering andclassification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given     

Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and expert assessment by Russian scientists with extensive field experience in the FSU. Overall, this method of image classification using GVI data appears to describe accurately regions with similar vegetation and hind cover across the FSU. Some questions regarding the identification of wetlands and potential problems with classification in the Russian high arctic are discussed. The products of this research will help improve carbon budget estimates of the FSU by providing accurate delineation and definition of carbon quantifiable regions.     

An unsupervised method of classifying remotely sensed images using Kohonen self‐organizing maps and agglomerative hierarchical clustering methods

Unlike conventional unsupervised classification methods, such as K‐means and ISODATA, which are based on partitional clustering techniques, the methodology proposed in this work attempts to take advantage of the properties of Kohonen's self‐organizing map (SOM) together with agglomerative hierarchical clustering methods to perform the automatic classification of remotely sensed images. The key point of the proposed method is to execute the cluster analysis process by means of a set of SOM prototypes, instead of working directly with the original patterns of the image. This strategy significantly reduces the complexity of the data analysis, making it possible to use techniques that have not normally been considered viable in the processing of remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Through the use of the SOM, the proposed method maps the original patterns of the image to a two‐dimensional neural grid, attempting to preserve the probability distribution and topology of the input space. Afterwards, an agglomerative hierarchical clustering method with restricted connectivity is applied to the trained neural grid, generating a simplified dendrogram for the image data. Utilizing SOM statistic properties, the method employs modified versions of cluster validation indices to automatically determine the ideal number of clusters for the image. The experimental results show examples of the application of the proposed methodology and compare its performance to the K‐means algorithm.     

Machine processing of LANDSAT data for soil survey: the Benue Valley savanna case study

A ground-truth (GT) map, produced by fieldwork and the interpretation of large-scale black and white aerial photographs, was compared with digital/graphical output from LANDSAT data of the savanna around Makurdi in central Nigeria. The GT maps were digitized, grid-converted and then aggregated while the LANDSAT data were resampled for the purpose of rectification on the ERDAS 400 microcomputer at the Michigan State University. A visual comparison of the maps was done by overlaying the digital maps on the GT map using a colour monitor while the histogram listing provided an approximate quantitative comparison.

The two algorithms of supervised classification (maximum likelihood and minimum distance) produced similar results but the third unsupervised classifier algorithm, cluster analysis, produced a far simpler map that is ideal as a reconnaissance soil/resource survey map. The major landforms were recognized by image processing but the reflectance-based classification resulted in misgroupings because of (i) the predominant influence of soil drainage regime on reflectance characteristics (thus well-drained soils tend to be grouped together just as were poorly drained soils, no matter what the intrinsic internal differences) and (ii) narrow units were masked by surrounding pixels and were therefore wrongly classified (a function of pixel size being dependent on LANDSAT's IFOV).

The vegetation cover of the tropical savanna is a major problem in the digital classification of soils.     

基于ISODATA算法的彩色图像分割

基于统计模式识别中非监督学习动态聚类算法ISODATA算法^[1]，将彩色图像（RGB空间）中由像素的R、G、B分量组成的三维矢量看作待分类的模式样本，定义相似度函数作为类间合并和分裂的依据，通过设定初始参数进行迭代，考虑聚类算法应用于图像分割的一些具体问题并加以改进，最后将图像分割成互不相交的区域，取得了良好的效果。     

Detection of land use/land cover changes for the northern part of the Nile delta (Burullus region), Egypt

The monitoring of land use/land cover changes along the north part of the Nile delta, Egypt is very important for the planner, management, governmental and non-governmental organizations and the scientific community. This information is essential for planning and implementing policies to optimize the use of natural resources and accommodate development whilst minimizing the impact on the environment. To study these changes along the study area, two sets of Landsat Thematic Mapper (TM) data were used. TM data are useful for this type of study due to its high spatial resolution, spectral resolution and low repetitive acquisition (16 days). A post-classification technique is used in this study based on hybrid classification (unsupervised and supervised). Each method used was assessed, and checked in field. Nine land use/land cover classes are produced. The overall accuracy for a 1984 image is 78% and for a 1997 image is 80%. The objective of this study was to detect land use/land cover changes, and to assess the nature of future change following construction of the international coastal road which crosses the study area.