Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

THE PHYLOGENETIC-RELATIONSHIPS AND CLASSIFICATION OF THE DOUCS AND SNUB-NOSED LANGURS OF CHINA AND VIETNAM

The taxonomy of the douc and snub-nosed langurs has changed several times during the 20th century. The controversy over the systematic position of these animals has been due in part to difficulties in studying them: both the doucs and the snub-nosed langurs are rare in the wild and are generally poorly represented in institutional collections. This review is based on a detailed examination of relatively large numbers of specimens of most of the species of langurs concerned. An attempt was made to draw upon as many types of information as were available in order to make an assessment of the phyletic relationships between the langur species under discussion. Toward this end, quantitative and qualitative features of the skeleton, specific features of visceral anatomy and characteristics of the pelage were utilized. The final data matrix comprised 178 characters. The matrix was analyzed using the program Hennig86. The results of the analysis support the following conclusions: (1) that the douc and snub-nosed langurs are generically distinct and should be referred to as species of Pygathrix and Rhinopithecus, respectively; (2) that the Tonkin snub-nosed langur be placed in its own subgenus as Rhinopithecus (Presbytiscus) avunculus and that the Chinese snub-nosed langur thus be placed in the subgenus Rhinopithecus (Rhinopithecus); (3) that four extant species of Rhinopithecus be recognized: R. (Rhinopithecus) roxellana Milne Edwards, 1870; R. (Rhinopithecus) bieti Milne Edwards, 1897; R. (Rhinopithecus) brelichi Thomas, 1903, and R. (Presbytiscus) avunculus Dollman, 1912; (4) that the Chinese snub-nosed langurs fall into northern and southern subgroups divided by the Yangtze river; (5) that R. lantianensis Hu and Qi, 1978, is a valid fossil species, and (6) the precise affinities and taxonomic status of the fossil species R. tingianus Matthew and Granger, 1923, are unclear because the type specimen is a subadult.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Classification of jinjiang oysters Crassostrea rivularis (Gould, 1861) from China, based on morphology and phylogenetic analysis

The jinjiang oyster Crassostrea rivularis [Gould, 1861. Descriptions of Shells collected in the North Pacific Exploring Expedition under Captains Ringgold and Rodgers. Proc. Boston Soc. Nat. Hist. 8 (April) 33-40] is one of the most important and best-known oysters in China. Based on the color of its flesh, two forms of C rivularis are recognized and referred to as the "white meat" and 11 red meat" oysters. The classification of white and red forms of this species has been a subject of confusion and debate in China. To clarify the taxonomic status of the two forms of C. rivularis, we collected and analyzed oysters from five locations along China's coast using both morphological characters and DNA sequences from mitochondrial 16S rRNA and cytochrome oxidase 1, and the nuclear 28S rRNA genes. Oysters were classified as white or red forms according to their morphological characteristics and then subjected to DNA sequencing. Both morphological and DNA sequence data suggest that the red and white oysters are two separate species. Phylogenetic analysis of DNA sequences obtained in this study and existing sequences of reference species show that the red oyster is the same species as C. ariakensis Wakiya [1929. Japanese food oysters. Jpn. J. Zool. 2, 359-367.], albeit the red oysters from north and south China are genetically distinctive. The white oyster is the same species as a newly described species from Hong Kong, C. hongkongensis Lam and Morton [2003. Mitochondrial DNA and identification of a new species of Crassostrea (Bivalvia: Ostreidae) cultured for centuries in the Pearl River Delta, Hong Kong, China. Aqua. 228, 1-13]. Although the name C. rivularis has seniority over C. ariakensis and C. hongkongensis, the original description of Ostrea rivularis by Gould [1861] does not fit shell characteristics of either the red or the white oysters. We propose that the name of C. rivularis Gould [1861] should be suspended, the red oyster should take the name C. ariakensis, and the white oyster should take the name C. hongkongensis. (C) 2004 Elsevier B.V. All rights reserved.

Classification of common oysters from North China

Oysters are commonly found on rocky shores along China's northern coast, although there is considerable confusion as to what species they are. To determine the taxonomic status of these oysters, we collected specimens from nine locations north of the Yangtze River and conducted genetic identification using DNA sequences. Fragments from three genes, mitochondrial 165 rRNA, mitochondria! cytochrome oxidase I (COI), and nuclear 285 rRNA, were sequenced in six oysters from each of the nine sites. Phylogenetic analysis of all three gene fragments clearly demonstrated that the small oysters commonly found on intertidal rocks in north China are Crassostrea gigas (Thunberg, 1793), not C. plicatula (the zhe oyster) as widely assumed. Their small size and irregular shell characteristics are reflections of the stressful intertidal environment they live in and not reliable characters for classification. Our study confirms that the oysters from Weifang, referred to as Jinjiang oysters or C. rivularis (Gould, 1861), are C. ariakensis (Wakiya, 1929). We found no evidence for the existence of C. talienwhanensis (Crosse, 1862) and other Crassostrea species in north China. Our study highlights the need for reclassifying oysters of China with molecular data.