Qihao Weng

A survey of image classification methods and techniques for improving classification performance

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.

A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China

The Zhujiang Delta of South China has experienced a rapid urban expansion over the past two decades due to accelerated economic growth. This paper reports an investigation into the application of the integration of remote sensing and geographic information systems (GIS) for detecting urban growth and assessing its impact on surface temperature in the region. Remote sensing techniques were used to carry out land use/cover change detection by using multitemporal Landsat Thematic Mapper data. Urban growth patterns were analysed by using a GIS-based modelling approach. The integration of remote sensing and GIS was further applied to examine the impact of urban growth on surface temperatures. The results revealed a notable and uneven urban growth in the study area. This urban development had raised surface radiant temperature by 13.01 K in the urbanized area. The integration of remote sensing and GIS was found to be effective in monitoring and analysing urban growth patterns, and in evaluating urbanization impact on surface temperature.

Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery

This paper examines characteristics of urban land-use and land-cover (LULC) classes using spectral mixture analysis (SMA), and develops a conceptual model for characterizing urban LULC patterns. A Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City was used in this research and a minimum noise fraction (MNF) transform was employed to convert the ETM+ image into principal components. Five image endmembers (shade, green vegetation, impervious surface, dry soil, and dark soil) were selected, and an unconstrained least-squares solution was used to un-mix the MNF components into fraction images. Different combinations of three or four endmembers were evaluated. The best fraction images were chosen to classify LULC classes based on a hybrid procedure that combined maximum-likelihood and decision-tree algorithms. The results indicate that the SMAbased approach significantly improved classification accuracy as compared to the maximum-likelihood classifier. The fraction images were found to be effective for characterizing the urban landscape patterns.

Fractal Analysis of Satellite-Detected Urban Heat Island Effect

This study reports on a study of the urban heat island (UHI) phenomenon in Guangzhou, China. In the study, surface radiant temperatures derived from Landsat TM thermal infrared images of December, 13, 1989, March 3, 1996, and August 29, 1997 were used. To examine the spatial distribution of surface radiant temperatures, transects were drawn and analyzed from each temperature image. The fractal dimensions of these transects were then computed using the divider method, to better understand the spatial variability of surface radiant temperatures caused by the thermal behavior of different land-cover types and landscape pattern characteristics. The effect of urban development on the geographical distribution of surface radiant temperatures and thus on the UHI was also investigated. The results revealed two major heat islands, one in the southwest and the other in the east of the city. The distribution of the UHIs was closely associated with industrial land uses but not with residential land uses. Changes in fractal dimension in different seasons were not only attributed to solar illumination and climatologic conditions relating to soil moisture and air temperature, but also to the topographic variation and the spatial arrangement and extent of different land cover types. Urban development increased the spatial variability of radiant temperatures, resulting in higher fractal dimension values.

A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban landscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land surface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were utilized to document the historical morphological changes in impervious surface and vegetation coverage and to analyze the relationship between these changes and those occurred in LST. Three fraction endmembers, i.e., impervious surface, green vegetation, and shade, were derived with an unconstrained least-squares solution. A hybrid classification procedure, which combined maximum-likelihood and decision-tree algorithms, was developed to classify the fraction images into land use and land cover classes. Correlation analyses were conducted to investigate the changing relationships of LST with impervious surface and vegetation coverage. Results indicate that multi-temporal fraction images were effective for quantifying the dynamics of urban morphology and for deriving a reliable measurement of environmental variables such as vegetation abundance and impervious surface coverage. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LSTs correlations with both impervious and vegetation coverage. Further researches should be directed to refine spectral mixture modeling by stratification, and by the use of multiple endmembers and hyperspectral imagery.

Extraction of urban impervious surfaces from an IKONOS image

Impervious surface has been recognised as an important indicator in urban environmental assessment. However, accurate extraction of impervious surface information in urban areas is a challenge because of the complexity of impervious materials. This paper explores different approaches for impervious surface extraction with IKONOS imagery in Indianapolis, U.S.A., by using decision tree classifier (DTC) and linear spectral mixture analysis (LSMA). This research indicates that DTC is an effective approach for extraction of different impervious surface classes, including high‐, medium‐ and low‐reflectivity impervious surfaces and that LSMA‐based approach can provide quantitative measure of imperviousness. A critical step is to separate dark impervious objects/features from shadows cast by tall buildings and tree canopy and from water.

Urban classification using full spectral information of landsat ETM+ imagery in Marion County, Indiana

This paper compares different image processing routines to identify suitable remote sensing variables for urban classifi- cation in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image. The ETMmultispectral, panchromatic, and thermal images are used. Incorporation of spectral signature, texture, and surface temperature is examined, as well as data fusion techniques for combining a higher spatial resolution image with lower spatial resolution multispectral images. Results indicate that incorporation of texture from lower spatial resolution images or of a temperature image cannot improve classification accuracies. However, incorporation of textures derived from a higher spatial resolution panchromatic image improves the classification accuracy. In particular, use of data fusion result and texture image yields the best classifi- cation accuracy with an overall accuracy of 78 percent and a kappa index of 0.73 for eleven land use and land cover classes.

Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data

This paper develops a methodology for integration of remote sensing and census data within a GIS framework to assess the quality of life in Indianapolis, Indiana, United States. Environmental variables, i.e. greenness, impervious surface and temperature, were derived from a Landsat ETM+ image. Socio‐economic variables, including population density, income, poverty, employment rate, education level and house characteristics from US census 2000, were integrated with the environmental variables at the block group level to derive indicators of quality of life. Pearsons correlation was computed to analyse the relationships among the variables. Further, factor analysis was conducted to extract unique information from the combined dataset. Three factors were identified and interpreted as material welfare, environmental conditions and crowdedness respectively. Each factor was viewed as a unique aspect of the quality of life. A synthetic index of the urban quality of life was created and mapped based on weighted factor scores of the three factors. Finally, regression models were built to estimate the quality of life in the city of Indianapolis based on selected environmental and socioeconomic variables.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO‐1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high‐quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI‐derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion‐derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low‐albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low‐albedo surfaces came mainly from additional bands in the mid‐infrared region.