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.

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.

Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

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.

Dietary-morphological relationships of fishes in Liangzi Lake, China

Species in Liangzi Lake were clustered into four trophic groups: Hemiramphus kurumeus and Hemiculter bleekeri bleekeri fed predominantly on terrestrial insects; Carassius auratus auratus and Abbottina rivularis on non-animal food; Hypseleotris swinhonis, Ctenogobius giurinus, Pseudorasbora parva and Toxabramis swinhonis on cladocerans or copepods; Culterichthys erythropterus on decapod shrimps. Gut length, mouth width, mouth height, gill raker length and gill raker spacing, varied widely among species. With the exception of three species pairs (H. swinhonis, C. glurinus; C. erythropterus, H. kurumeus; T. swinhonis, H. bleekeri bleekeri), principal components analysis of morphological variables revealed over-dispersion of species. Canonical correspondence analysis of dietary and morphological data revealed five significant dietary-morphological correlations. The first three roots explained > 85% of the total variance. The first root reflected mainly the relationship of gut length to non-animal feud, with an increase in gut length associated with an increase in non-animal food. The second root was influenced strongly by the relationship of the gill raker spacing to consumption of copepods, with an increase in gill raker spacing associated positively with copepods in the diet. The third root was influenced by the relationship of mouth gape to consumption of fish and decapod shrimps, with an increase in mouth gape associated with more fish and decapod shrimps in the diet. These significant dietary-morphological relationships supported the eco-morphological hypotheses that fish morphology influence food use, and morphological variation is important in determining ecological segregation of co-existing fish species. (C) 2001 The Fisheries Society of the British Isles.

PenBase, the shrimp antimicrobial peptide penaeidin database: Sequence-based classification and recommended nomenclature

Antimicrobial peptides play a major role in innate immunity. The penaeidins, initially characterized from the shrimp Litopenaeus vannamei, are a family of antimicrobial peptides that appear to be expressed in all penaeid shrimps. As of recent, a large number of penaeid nucleotide sequences have been identified from a variety of penaeid shrimp species and these sequences currently reside in several databases under unique identifiers with no nomenclatural continuity. To facilitate research in this field and avoid potential confusion due to a diverse number of nomenclatural designations, we have made a systematic effort to collect, analyse, and classify all the penaeidin sequences available in every database. We have identified a common penaeidin signature and subsequently established a classification based on amino acid sequences. In order to clarify the naming process, we have introduced a 'penaeidin nomenclature' that can be applied to all extant and future penaeidins. A specialized database, PenBase, which is freely available at http://www.penbase.immunaqua.com, has been developed for the penaeidin family of antimicrobial peptides, to provide comprehensive information about their properties, diversity and nomenclature. (c) 2005 Elsevier Ltd. All rights reserved.

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.

三种海水经济鱼类早期发育生物学的研究

海水经济鱼类的养殖在我国已经形成第四次海水养殖浪潮，经济效益显著，有力地推动了我国海水养殖的产业结构调整和可持续发展。然而在海水养殖发展过程中也存在着诸多问题，尤其是早期发育阶段的高死亡率，严重制约了我国海水养殖产业的稳定和健康发展。 海水鱼类养殖的关键为高质量，高存活率苗种的生产和培育，由于鱼类种类繁多，生物多样性丰富，对应实际的繁育技术，尤其是新品种的开发，必须要做出相应的调整。这就要求我们必须对每一种鱼类早期发育有所了解，并将形态和组织上的数据用于指导生产。 本文通过显微观察和组织学研究，主要描述和研究了我国北方三种重要的海水经济鱼类(条斑星鲽、杂交鲆、条石鲷)的早期发育生物学，并结合实际生产进一步阐明关键期的产生原因，机理以及采用相应的对策。具体结果如下： 1.条斑星鲽：作为冷温性鲆鲽鱼类，条斑星鲽早期发育过程的特征主要有： ① 条斑星鲽受精卵无油球，卵子呈半浮性；不同步卵裂现象提前，发生在第三次卵裂；卵裂期裂球大小差异大。孵化过程较长，在水温8 ± 0.3℃，盐度33的条件下，经9 d孵化。条斑星鲽胚胎发育的不同时期对温度的敏感性不同，其中原肠期对温度比较敏感。 ②在8-10℃，盐度33的条件下，8-9 dph开口摄食。且开口时，其吻前端出现有一点状黑褐色素，构成了条斑星鲽仔鱼“开口期”的重要标志。卵黄囊于消失。在后期仔鱼末期，背鳍和臀鳍上形成特有的黑褐色条斑带。 ③杯状细胞首先出现在咽腔后部和食道前段，胃腺和幽门盲囊出现于29 dph，变态期始于30dph。在条斑星鲽早期发育过程中，观察到其直肠粘膜层细胞质出现大量嗜伊红颗粒，为仔鱼肠道上皮吸收的蛋白质。 ④首先淋巴化的免疫器官是头肾，然后是胸腺和脾脏，这与大部分硬骨鱼类不同。条斑星鲽除头肾和脾脏外，胸腺实质也形成MMCs。其中以脾脏形成MMCs最为丰富，形态多样。 2. 杂交鲆：为同属的牙鲆和夏鲆间的远缘杂交种，其发育过程的特点为： ① 在温度为15.4～16.0℃，杂交鲆胚胎从受精到孵化所需的时间为76 h左右，胚孔关闭前期，胚胎先出现视囊及克氏囊，而后形成体节。孵出前胚体在卵膜内环绕不到1周。 ② 孵化后消失。杂交鲆群体变态间隔长(34-60 dph)，且变态高峰期出现的冠状幼鳍不明显(与母本牙鲆相比)，数量为7-8根。 ③组织学观察发现，其消化系统中胃腺出现较晚，且胃腺发育过程缓慢(与母本牙鲆相比)。甲状腺滤泡增生不明显，颜色较浅，数量较少。杂交鲆在早期发育过程中，并没有出现鳔原基。 3. 条石鲷作为岩礁性的暖水性鱼类，早期发育过程也较为特殊，包括外形以及内部的器官结构。主要特点有： ① 受精卵：受精卵卵黄上具有龟裂结构，为鱼卵的分类特征之一。 ② 初孵仔鱼：初孵仔鱼背鳍膜上的黑色素，从体背面向背鳍膜边缘移动，到3dph仔鱼基本消失，此为本种仔鱼发育所特有的特点。 ③ 后期仔鱼和稚鱼：肠道肌肉层加厚明显，仔稚鱼胃肠排空率急剧上升，死亡率增加，通过改善常规的投饵方式部分解决了这个死亡高峰的问题。在幼鱼初期，牙齿融合为骨喙，为石鲷科鱼类的特征。 ④胸腺上皮分泌细胞：类似的现象同样在虹鳟鱼中发现，但是虹鳟鱼胸腺上皮分泌细胞不如条石鲷的丰富，同样也不如条石鲷的排列整齐，而是零星分布在胸腺上皮与咽腔接触的表面。除了正常的造血器官—脾脏和头肾外，肝脏、胰腺和鳔等多种组织等也出现MMCs，此现象在硬骨鱼类不多见，一般发生在软骨鱼类。