A land‐cover classification for landslide susceptibility mapping by using feature components

Classifying original bands and/or image components may cause unsatisfactory results in fields that have heterogeneous reflectance. In such cases, the demand for accurate land‐use, land‐cover, vegetation, and forestry information may require more specific components. The components should represent peculiar information collected from several inputs for target land covers. In this study, a new technique of land‐cover classification was explored to prepare an input which increases the success of landslide susceptibility mapping in a subtropical region, Asarsuyu Catchment Area (Duzce). Land‐cover mapping is a difficult issue in this area by only carrying out field studies and aerial‐photo interpretations. Moreover, applying different classifications of Landsat Thematic Mapper bands and/or their secondary products does not produce acceptable results. For this reason, vegetation indices, soil/surface moisture indices, topographic wetness index and drainage density were calculated to produce feature representative components for the land‐cover classification process. Results obtained from the proposed technique show that feature representative components significantly improve the conventional classification accuracy from 77% to 89% and the resultant land‐cover map is such a valuable input for landslide susceptibility mapping that it increases the success of the landslide susceptibility map from 63% to 88%.     

Optimization in multi‐scale segmentation of high‐resolution satellite images for artificial feature recognition

Multi‐resolution segmentation, as one of the most popular approaches in object‐oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi‐resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm‐associated parameters in multi‐resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi‐spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature‐type‐oriented segmentation evaluation provides an insight to the decision‐making process in choosing appropriate parameters towards a high‐quality segmentation. By adopting these feature‐type‐based optimal parameters, multi‐resolution segmentation is able to produce objects of desired form to represent artificial features.     

Speech emotion recognition based on multi‐feature and multi‐lingual fusion

Multimedia Tools and Applications - A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused bylack of...     

Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability

An object‐based approach was utilized in the development of two urban land‐cover classification schemes on high‐resolution (0.6 m), true‐colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest‐neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy‐rule set, creating a product transportable to different areas of the same imagery, or for land‐cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule‐based classification. We conclude that the transportable classification scheme is satisfactory for general land‐cover analyses; yet classification accuracy can be enhanced at site‐specific venues with the incorporation of nearest‐neighbour functions using class samples.     

Sparse margin–based discriminant analysis for feature extraction

The existing margin-based discriminant analysis methods such as nonparametric discriminant analysis use K-nearest neighbor (K-NN) technique to characterize the margin. The manifold learning–based methods use K-NN technique to characterize the local structure. These methods encounter a common problem, that is, the nearest neighbor parameter K should be chosen in advance. How to choose an optimal K is a theoretically difficult problem. In this paper, we present a new margin characterization method named sparse margin–based discriminant analysis (SMDA) using the sparse representation. SMDA can successfully avoid the difficulty of parameter selection. Sparse representation can be considered as a generalization of K-NN technique. For a test sample, it can adaptively select the training samples that give the most compact representation. We characterize the margin by sparse representation. The proposed method is evaluated by using AR, Extended Yale B database, and the CENPARMI handwritten numeral database. Experimental results show the effectiveness of the proposed method; its performance is better than some other state-of-the-art feature extraction methods.     

DEM and bathymetry estimation for mapping a tide‐coordinated shoreline from fine spatial resolution satellite sensor imagery

Fine spatial resolution remotely sensed imagery has considerable potential for mapping a shoreline. Although fine spatial resolution imagery typically allows the instantaneous shoreline to be mapped with high accuracy, interest is normally focused on a reference shoreline, defined on a stable vertical datum, which is generally not apparent in the imagery unless acquired at a time carefully coordinated with the tidal characteristics of the region. To map a tide‐coordinated shoreline, such as the mean sea level (MSL), information on terrain topography, bathymetry and tidal characteristics is required. In this study, IKONOS imagery was used to derive topographic and bathymetric information for an extract of the Malaysian coast and combined with a tide chart for the region to map the MSL. The digital elevation model (DEM) derived had a root mean square error (RMSE), calculated on independent control points, of ～2.2 m while the bathymetric model had an RMSE of 0.87 m. The shoreline derived from the combination of the DEM, bathymetry and tidal information was mapped with an RMSE of 1.8 m.     

Left‐handed scrolling for pen‐based devices

The effectiveness of interaction with mobile devices can be impacted by handedness; however, support for handedness in the interface is rarely provided. The goal of this article is to demonstrate that handedness is a significant interface consideration that should not be overlooked. Four studies were conducted to explore left‐handed user interaction with right‐ or left‐aligned scrollbars on personal digital assistants. Analysis of the data shows that left‐handed users are able to select targets significantly faster using a left‐aligned scrollbar when compared to a right‐aligned scrollbar. User feedback also indicated that a left‐aligned scrollbar was preferred by left‐handed users and provided more natural interaction.     

Designing fuzzy rule based classifier using self‐organizing feature map for analysis of multispectral satellite images

We propose a novel scheme for designing fuzzy rule based classifiers. A self‐organizing feature map (SOFM) based method is used for generating a set of prototypes, which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different contexts leading to context sensitive inferencing. To realize context sensitive inferencing we used a softmin operator with a tuneable parameter. The proposed scheme is tested on several multispectral satellite image datasets and the performance is found to be much better than the results reported in the literature.     

Envisat multi‐polarized ASAR data for flood mapping

During the August 2002 Elbe river flood, different satellite sensor data were acquired, and especially Envisat Advanced Synthetic Aperture Radar (ASAR) data. The ASAR instrument was activated in Alternating Polarization (AP) and Image (IM) modes, providing high resolution datasets. Thus, the comparison with a quasi‐simultaneous ERS‐2 scene enables the evaluation of the contribution of polarization configurations to flood boundary delineation. This study highlights the increased capabilities of the Envisat ASAR instrument in flood mapping, especially the benefit of combining like‐ and cross‐polarizations for rapid mapping within a crisis context.     

Radar automatic target recognition based on feature extraction for complex HRRP

Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.     

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.     

Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey,Turkey

Three Landsat7 ETM+ images acquired in May, July and August during the 2000 crop growing season were used for field‐based mapping of summer crops in Karacabey, Turkey. First, the classification of each image date was performed on a standard per pixel basis. The results of per pixel classification were integrated with digital agricultural field boundaries and a crop type was determined for each field based on the modal class calculated within the field. The classification accuracy was computed by comparing the reference data, field‐by‐field, to each classified image. The individual crop accuracies were examined on each classified data and those crops whose accuracy exceeds a preset threshold level were determined. A sequential masking classification procedure was then performed using the three image dates, excluding after each classification the class properly classified. The final classified data were analysed on a field basis to assign each field a class label. An immediate update of the database was provided by directly entering the results of the analysis into the database. The sequential masking procedure for field‐based crop mapping improved the overall accuracies of the classifications of the July and August images alone by more than 10%.     

Artificial immune‐based supervised classifier for land‐cover classification

This paper explores the potential of an artificial immune‐based supervised classification algorithm for land‐cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non‐self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune‐based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune‐based classifier works well in comparison with the maximum likelihood and the decision‐tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision‐tree classification algorithms. Results suggest an improved performance with the immune‐based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune‐based supervised classifier requires several user‐defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant.     

Fuzzy segmentation for object‐based image classification

This Letter proposes an object‐based image classification procedure which is based on fuzzy image‐regions instead of crisp image‐objects. The approach has three stages: (a) fuzzification in which fuzzy image‐regions are developed, resulting in a set of images whose digital values express the degree of membership of each pixel to target land‐cover classes; (b) feature analysis in which contextual properties of fuzzy image‐regions are quantified; and (c) defuzzification in which fuzzy image‐regions are allocated to target land‐cover classes. The proposed procedure is implemented using automated statistical techniques that require very little user interaction. The results indicate that fuzzy segmentation‐based methods produce acceptable thematic accuracy and could represent a viable alternative to current crisp image segmentation approaches.     

An efficient orientation distance–based discriminative feature extraction method for multi-classification

Feature extraction is an important step before actual learning. Although many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. This paper proposes a novel feature extraction method, called orientation distance–based discriminative (ODD) feature extraction, particularly designed for multi-class classification problems. Our proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine an appropriate kernel function and map the input data with all classes into a feature space where the classes of the data are well separated. In the second step, we put forward two variants of ODD features, i.e., one-vs-all-based ODD and one-vs-one-based ODD features. We first construct hyper-plane (SVM) based on one-vs-all scheme or one-vs-one scheme in the feature space; we then extract one-vs-all-based or one-vs-one-based ODD features between a sample and each hyper-plane. These newly extracted ODD features are treated as the representative features and are thereafter used in the subsequent classification phase. Extensive experiments have been conducted to investigate the performance of one-vs-all-based and one-vs-one-based ODD features for multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods.     

A Web‐based multi‐agent system for interpreting medical images

A difficult problem in medical image interpretation is that for every image type such as xray and every body organ such as heart, there exist specific solutions that do not allow for generalization. Just collecting all the specific solutions will not achieve the vision of a computerized physician. To address this problem, we develop an intelligent agent approach based on the concept of active fusion and agentoriented programming. The advantage of agentoriented programming is that it combines the benefits of objectoriented programming and expert system. Following this approach, we develop a Webbased multiagent system for interpreting medical images. The system is composed of two major types of intelligent agents: radiologist agents and patient representative agents. A radiologist agent decomposes the image interpretation task into smaller subtasks, uses multiple agents to solve the subtasks, and combines the solutions to the subtasks intelligently to solve the image interpretation problem. A patient representative agent takes questions from the user (usually a patient) through a Webbased interface, asks for multiple opinions from radiologist agents in interpreting a given set of images, and then integrates the opinions for the user. In addition, a patient representative agent can answer questions based on the information in a medical information database. To maximize the satisfaction that patients receive, the patient representative agents must be as informative and timely as communicating with a human. With an efficient pseudonatural language processing, a knowledge base in XML, and user communication through Microsoft Agent, the patient representative agents can answer questions effectively.     

Deformation based feature selection for Computer Aided Diagnosis of Alzheimer’s Disease

Deformation-based Morphometry (DBM) allows detection of significant morphological differences of brain anatomy, such as those related to brain atrophy in Alzheimer’s Disease (AD). DBM process is as follows: First, performs the non-linear registration of a subject’s structural MRI volume to a reference template. Second, computes scalar measures of the registration’s deformation field. Third, performs across volume statistical group analysis of these scalar measures to detect effects. In this paper we use the scalar deformation measures for Computer Aided Diagnosis (CAD) systems for AD. Specifically this paper deals with feature extraction methods over five such scalar measures. We evaluate three supervised feature selection methods based on voxel site significance measures given by Pearson correlation, Bhattacharyya distance and Welch’s t-test, respectively. The CAD system discriminating between healthy control subjects (HC) and AD patients consists of a Support Vector Machine (SVM) classifier trained on the DBM selected features. The paper reports experimental results on structural MRI data from the cross-sectional OASIS database. Average 10-fold cross-validation classification results are comparable or improve the state-of-the-art results of other approaches performing CAD from structural MRI data. Localization in the brain of the most discriminant deformation voxel sites is in agreement with findings reported in the literature.     

Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models

Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery. However, for waterline mapping problems, the traditional spatial dependence principle of subpixel mapping is not suitable as the water flow is always controlled by the topography. This letter presents a novel algorithm based on a high spatial resolution digital elevation model (DEM) to address the subpixel waterline mapping problem. The waterline was mapped at the subpixel scale with a proposed rule according to the physical features of the water flow and additional information provided by the DEM. The method was evaluated with degraded real remotely sensed imagery at different spatial resolutions. The results show that the proposed method can provide more accurate classifications than the traditional subpixel mapping method. Moreover, the fine spatial resolution DEM can be used as feasible supplementary data for subpixel waterline mapping from coarser spatial resolution imagery.     

MISR multi‐angular spectral remote sensing for temperate forest mapping at 1.1‐km resolution

Accurate information about temperate forest distribution and extent is important to quantify the carbon sink in the northern temperate forest. While Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) multi‐temporal spectral information has been extensively studied for this purpose, it has not been fully studied whether Multi‐angle Imaging Spectroradiometer (MISR) information is helpful for temperate forest mapping at 1.1‐km resolution. This Letter addresses the potential use of 1.1‐km multi‐angular MISR data to improve temperate forest mapping based on a study area in eastern USA. Classification accuracy using nadir‐only MISR data is compared with results derived from the combined use of some off‐nadir MISR data. The results show a substantial increase in forest mapping accuracy when off‐nadir spectral measurements are used.