Probability estimation for multi-class classification using AdaBoost

It is a general viewpoint that AdaBoost classifier has excellent performance on classification problems but could not produce good probability estimations. In this paper we put forward a theoretical analysis of probability estimation model and present some verification experiments, which indicate that AdaBoost can be used for probability estimation. With the theory, we suggest some useful measures for using AdaBoost algorithms properly. And then we deduce a probability estimation model for multi-class classification by pairwise coupling. Unlike previous approximate methods, we provide an analytical solution instead of a special iterative procedure. Moreover, a new problem that how to get a robust prediction with classifier scores is proposed. Experiments show that the traditional predict framework, which chooses one with the highest score from all classes as the prediction, is not always good while our model performs well.     

基于最大最小距离的高光谱遥感图像波段选择

为减少高光谱遥感图像光谱空间冗余,降低后续处理的计算复杂度,提出一种基于最大最小距离的高光谱图像波段选择算法。首先计算波段标准差,选定标准差最大的波段作为初始中心;然后使用最大最小距离算法得到相对距离较远的聚类中心,对波段进行聚类;最后使用K中心点算法更新聚类中心。实验仿真结果表明:通过基于最大最小距离算法选择的波段,能够选出同时满足信息量大、相关性小的要求的波段子集,并将获得的波段组合用于高光谱图像分类时,可以得到较好的分类精度。     

Multi-spectral classification of snow using NOAA AVHRR imagery

Abstract

Problems of accurate discrimination between snow and cloud, together with the detection of the snow pack boundary, have handicapped the use of satellite data in operational snow-cover mapping systems. A technique, involving an unsupervised clustering procedure, is described which allows the removal of cloud areas using NOAA-9 Advanced Very High Resolution Radiometer (AVHRR) channel-1, channel-3 and channel-4 data in conditions of recent snow lie and a difference channel (channel-2 —channel-1 with channel-3 and channel-4) during periods of advanced snow melt. Accurate delineation of snow extent is provided by the techniques if these specified snow conditions are taken into account. A method for the identification of areas of marginal snow melt is also presented, based on comparisons with Landsat Thematic Mapper data. The classifications also enable the determination of snow areas influenced by cloud shadows and conifer forest in addition to separating areas of differing snow depth and percentage cover.     

Semi-supervised classification using multiple clusterings

Graph determines the performance of graph-based semi-supervised classification. In this paper, we investigate how to construct a graph from multiple clusterings and propose a method called Semi-Supervised Classification using Multiple Clusterings (SSCMC in short). SSCMC firstly projects original samples into different random subspaces and performs clustering on the projected samples. Then, it constructs a graph by setting an edge between two samples if these two samples are clustered in the same cluster for each clustering. Next, it combines these graphs into a composite graph and incorporates the resulting composite graph with a graph-based semi-supervised classifier based on local and global consistency. Our experimental results on two publicly available facial images show that SSCMC not only achieves higher accuracy than other related methods, but also is robust to input parameters.     

Mapping reedbed habitats using texture-based classification of QuickBird imagery

Many organisms rely on reedbed habitats for their existence, yet, over the past century there has been a drastic reduction in the area and quality of reedbeds in the UK due to intensified human activities. In order to develop management plans for conserving and expanding this threatened habitat, accurate up-to-date information is needed concerning its current distribution and status. This information is difficult to collect using field surveys because reedbeds exist as small patches that are sparsely distributed across landscapes. Hence, this study was undertaken to develop a methodology for accurately mapping reedbeds using very high resolution QuickBird satellite imagery. The objectives were to determine the optimum combination of textural and spectral measures for mapping reedbeds; to investigate the effect of the spatial resolution of the input data upon classification accuracy; to determine whether the maximum likelihood classifier (MLC) or artificial neural network (ANN) analysis produced the most accurate classification; and to investigate the potential of refining the reedbed classification using slope suitability filters produced from digital terrain data. The results indicate an increase in the accuracy of reedbed delineations when grey-level co-occurrence textural measures were combined with the spectral bands. The most effective combination of texture measures were entropy and angular second moment. Optimal reedbed and overall classification accuracies were achieved using a combination of pansharpened multispectral and texture images that had been spatially degraded from 0.6 to 4.8 m. Using the 4.8 m data set, the MLC produced higher classification accuracy for reedbeds than the ANN analysis. The application of slope suitability filters increased the classification accuracy of reedbeds from 71% to 79%. Hence, this study has demonstrated that it is possible to use high resolution multispectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, judicious selection of input bands, spatial resolution and classification algorithm and post-classification refinement using terrain data.     

Per-field classification: an example using SPOT HRV imagery

Abstract

The classification of land cover on remotely sensed imagery is usually undertaken in a per-pixel format within an image file or in a per-field format within a non-image file. The latter is more accurate but does not produce an image output and is not readily input to a vector-based geographical information system. We propose setting the pixels in each field to a representative statistic for that field and then using a per-pixel classifier to perform a per-field classification in an image file. This procedure was evaluated using SPOT high resolution visible (HRV) imagery. The highest classification accuracy of 62.1 per cent (12 class) was achieved using measures of prior probabilities and image texture within the proposed per-field format.     

Multitemporal land-cover classification of Mexico using Landsat MSS imagery

A complete land-cover classification of Mexico was performed using Landsat Multi-Spectral Scanner (MSS) imagery corresponding to years 1974, 1986 and 1990 ( - 1 y). The categorization of the approximately 2 M km 2 geographical region included the classification of approximately 300 equivalent scene images. Vegetation experts throughout the country provided an initial 250-class inventory of major vegetation associations by applying an unsupervised classification approach. A final regrouping was performed to produce a generalized thematic product containing 12 classes to provide a consistent national scale product. Classification accuracies were evaluated for each scene by means of cartographic comparison using two independently developed reference datasets corresponding to the 1970s and 1990s. An automated evaluation procedure was developed that incorporated decision rules to duplicate the results obtained using a manual accuracy assessment procedure. Overlaying both the image and the digital cartographic information allowed for the comparison of randomly selected pixels within each image scene. An overall accuracy for the three epochs of 62% was obtained for the 300 image scenes. Study results have provided a historical baseline documenting vegetation extent and distribution across Mexico over the two-decade period. This study serves as a possible model for subsequent North American land-cover characterization efforts.     

Land-use classification in central Spain using SIR-A and MSS imagery

Abstract

Methods for making more efficient use of synthetic aperture radar (SAR) imagery are considered. Local standard deviation and autocorrelation texture measures are used to provide information on the spatial variability in the scattering cross-section. Use of these statistics in a window of 180×180m improved classification success rates from 39 to 66 per cent with digitized shuttle imaging radar (S1R-A) data. Multispectral scanner (MSS) achieves 70 per cent success with the same window size and, by combining this with SAR, a 78 per cent success rate is reached.     

Genetic algorithm-based feature set partitioning for classification problems

Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a single useful subset of features. This paper presents a novel feature set partitioning approach that is based on a genetic algorithm. As part of this new approach a new encoding schema is also proposed and its properties are discussed. We examine the effectiveness of using a Vapnik–Chervonenkis dimension bound for evaluating the fitness function of multiple, oblivious tree classifiers. The new algorithm was tested on various datasets and the results indicate the superiority of the proposed algorithm to other methods.     

一种用于不平衡数据分类的改进AdaBoost算法

真实世界中存在大量的类别不平衡分类问题,传统的机器学习算法如AdaBoost算法,关注的是分类器的整体性能,而没有给予小类更多的关注。因此针对类别不平衡学习算法的研究是机器学习的一个重要方向。AsymBoost作为AdaBoost的一种改进算法,用于类别不平衡学习时,牺牲大类样本的识别精度来提高小类样本的分类性能。AsymBoost算法依然可能遭遇样本权重过大造成的过适应问题。据此提出了一种新型的AdaBoost改进算法。该方法通过对大类中分类困难样本的权重和标签进行处理,使分类器能够同时获得较好的查准率和查全率。实验结果表明,该方法可以有效提高在不平衡数据集上的分类性能。     

Crop and land cover classification in Iran using Landsat 7 imagery

Remote sensing provides one way of obtaining more accurate information on total cropped area and crop types in irrigated areas. The technique is particularly well suited to arid and semi‐arid areas where almost all vegetative growth is associated with irrigation. In order to obtain more information with regard to crop patterns in the irrigated areas in the Zayandeh Rud basin, a classification analysis was made of the Landsat 7 image of 2 July 2000. The target of the classification was to primarily focus on the agricultural land use. The date of the image fell in the transition period where the first crops were harvested and many fields were being prepared for the second crop. The image has therefore captured an instantaneous picture of a system generally in transition from the first to the second crop, but with significant differences from system to system, both with respect to crop types and agricultural cycles. The overall accuracy of image registration was about 30 m (one pixel). Fieldwork was conducted on various occasions in August–October 2000 and May–October 2001. Farmers were interviewed to determine the situation on 2 July 2000. Fields were mapped in detail with the GPS instruments, and data compiled for 112 fields. Using a supervised classification system, training areas were selected and initial classifications were made to determine the validity of the classes. After merging several classes and testing several new classes a final classification system was made. All seven Landsat bands were used in the determination of the feature statistics. The final classification was made with the minimum distance algorithm. The statistics with respect to areas and crop type for the districts was obtained by crossing the raster map with the irrigation district raster map. The results with respect to crop type and total irrigated area per district were compared with those of previous studies. This included both NOAA/AVHRR and conventional agricultural district statistics.     

Advances in classification for land cover mapping using SPOT HRV imagery

Abstract

High-resolution data from the HRV (High Resolution Visible) sensors onboard the SPOT-1 satellite have been utilized for mapping semi-natural and agricultural land cover using automated digital image classification algorithms. Two methods for improving classification performance are discussed. The first technique involves the use of digital terrain information to reduce the effects of topography on spectral information while the second technique involves the classification of land-cover types using training data derived from spectral feature space. Test areas in Snowdonia and the Somerset Levels were used to evaluate the methodology and promising results were achieved. However, the low classification accuracies obtained suggest that spectral classification alone is not a suitable tool to use in the mapping of semi-natural cover types.     

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

This study proposed a multi-scale, object-based classification analysis of SPOT-5 imagery to map Moso bamboo forest. A three-level hierarchical network of image objects was developed through multi-scale segmentation. By combining spectral and textural properties, both the classification tree and nearest neighbour classifiers were used to classify the image objects at Level 2 in the three-level object hierarchy. The feature selection results showed that most of the object features were related to the spectral properties for both the classification tree and nearest neighbour classifiers. Contextual information characterized by the composition of classified image objects using the class-related features assisted the detection of shadow areas at Levels 1 and 3. Better classification results were achieved using the nearest neighbour algorithm, with both the producer’s and user’s accuracy higher than 90% for Moso bamboo and an overall accuracy of over 85%. The object-based approach toward incorporating textural and contextual information in classification sequence at various scales shows promise in the analysis of forest ecosystems of a complex nature.     

基于GA-CFS和AdaBoost算法的网络流量分类

鉴于特征属性选择在网络流量分类中占据重要地位,为了确定最优特征子集,利用CFS作为适应度函数的改进遗传算法(GA-CFS),从网络流量的249个属性空间中提取主要属性并最终选定18个特征组合作为最优特征子集。通过AdaBoost算法把一系列的弱分类器提升为强分类器,对网络流量进行了深入的分类研究。实验结果表明,基于GA-CFS和AdaBoost的流量组合分类方法较弱分类器具有较高的分类准确率。     

A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

Abstract

The imaging frequency and synoptic coverage of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) make possible for the first time a phenological approach to vegetation cover classification in which classes are defined in terms of the timing, the duration and the intensity of photosynthetic activity. This approach, which exploits the strong, approximately linear relationship between the amount of solar irradiance absorbed by plant pigments and shortwave vegetation indices calculated from red and near-infrared reflectances, involves a supervised binary decision tree classification of phytophenological variables derived from multidate normalized difference vegetation index (NDVI) imagery. A global phytophenological classification derived from NOAA global vegetation index imagery is presented and discussed. Although interpretation of the various classes is limited considerably by the quality of global vegetation index imagery, the data show clearly the marked temporal asymmetry of terrestrial photosynthetic activity.     

改进的AdaBoost算法与SVM的组合分类器

提出了一种改进的AdaBoost算法与支持向量机组合的分类方法,用来处理多类别分类。采用规则抽样来解决支持向量机分类中正负样本的不平衡性,改进AdaBoost算法,使其在初始化时考虑样本分布稀疏的重要性,有利于稀有类样本的正确划分。实验结果表明,此方法与标准支持向量机分类器相比,泛化性能有一定程度的提高。     

Texture classification method using multiple space filling curves

Texture analysis by using the Peano curve does not provide sufficient discriminatory power to classify natural textures. An improved method by simultaneously using several space filling curves to the texture analysis and classification is proposed.     

Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm

Robust detection and tracking of pedestrians in image sequences are essential for many vision applications. In this paper, we propose a method to detect and track multiple pedestrians using motion, color information and the AdaBoost algorithm. Our approach detects pedestrians in a walking pose from a single camera on a mobile or stationary system. In the case of mobile systems, ego-motion of the camera is compensated for by corresponding feature sets. The region of interest is calculated by the difference image between two consecutive images using the compensated image. Pedestrian detector is learned by boosting a number of weak classifiers which are based on Histogram of Oriented Gradient (HOG) features. Pedestrians are tracked by block matching method using color information. Our tracking system can track pedestrians with possibly partial occlusions and without misses using information stored in advance even after occlusion is ended. The proposed approach has been tested on a number of image sequences, and was shown to detect and track multiple pedestrians very well.