Computing Local Fractal Dimension Using Geographical Weighting Scheme

The fractal dimension (D) of a surface can be viewed as a summary or average statistic for characterizing the geometric complexity of that surface. The D values are useful for measuring the geometric complexity of various land cover types. Existing fractal methods only calculate a single D value for representing the whole surface. However, the geometric complexity of a surface varies across patches and a single D value is insufficient to capture these detailed variations. Previous studies have calculated local D values using a moving window technique. The main purpose of this study is to compute local D values using an alternative way by incorporating the geographical weighting scheme within the original global fractal methods. Three original fractal methods are selected in this study: the Triangular Prism method, the Differential Box Counting method and the Fourier Power Spectral Density method. A Gaussian density kernel function is used for the local adaption purpose and various bandwidths are tested. The first part of this dissertation research explores and compares both of the global and local D values of these three methods using test images. The D value is computed for every single pixel across the image to show the surface complexity variation. In the second part of the dissertation, the main goal is to study two major U.S. cities located in two regions. New York City and Houston are compared using D values for both of spatial and temporal comparison. The results show that the geographical weighting scheme is suitable for calculating local D values but very sensitive to small bandwidths. New York City and Houston show similar global D results for both year of 2000 and 2016 indicating there were not much land cover changes during the study period

Multiscale Analysis for Characterization of Remotely Sensed Images.

In this study we addressed fundamental characteristics of image analysis in remote sensing, enumerated unavoidable problems in spectral analysis, and highlighted the spatial structure and features that increase information amount and measurement accuracy. We addressed the relationship between scale and spatial structure and the difficulties in characterizing them in complex remotely sensed images. We suggested that it is necessary to employ multiscale analysis techniques for analyzing and extracting information from remotely sensed images. We developed a multiscale characterization software system based on an existing software called ICAMS (Image Characterization And Modeling System), and applied the system to various test data sets including both simulated and real remote sensing data in order to evaluate the performance of these methods. In particular, we analyzed the fractal and wavelet methods. For the fractal methods, the results from using a set of simulated surfaces suggested that the triangular prism surface area method was the best technique for estimating the fractal dimension of remote sensing images. Through examining Landsat TM images of four different land covers, we found that fractal dimension and energy signatures derived from wavelets can measure some interesting aspects of the spatial content of remote sensing data, such as spatial complexity, spatial frequency, and textural orientation. Forest areas displayed the highest fractal dimension values, followed by coastal, urban, and agriculture respectively. However, fractal dimension by itself is insufficient for accurate classification of TM images. Wavelet analysis is more accurate for characterizing spatial structures. A longer wavelet was shown to be more accurate in the representation and discrimination of land-cover classes than a similar function of shorter length, and the combination of energy signatures from multiple decomposition levels and multispectral bands led to better characterization results than a single resolution and single band decomposition. Significant improvements in classification accuracy were achieved by using fractal dimensions in conjunction with the energy signature. This study has shown that multiscale analysis techniques are very useful to complement spectral classification techniques to extract information from remotely sensed images

A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification

Urban land-use/land-cover classification is entering a new era with the increased availability of high-resolution satellite imagery and new methods such as texture analysis and artificial intelligence classifiers. Recent research demonstrated exciting improvements of using fractal dimension, lacunarity, and Moran’s I in classification but the integration of these spatial metrics has seldom been investigated. Also, previous research focuses more on developing new classifiers than improving the robust, simple, and fast maximum likelihood classifier. The goal of this dissertation research is to develop a new approach that utilizes a texture vector (fractal dimension, lacunarity, and Moran’s I), combined with a new genetic Bayesian classifier, to improve urban land-use/land-cover classification accuracy. Examples of different land-use/land-covers using post-Katrina IKONOS imagery of New Orleans were demonstrated. Because previous geometric-step and arithmetic-step implementations of the triangular prism algorithm can result in significant unutilized pixels when measuring local fractal dimension, the divisor-step method was developed and found to yield more accurate estimation. In addition, a new lacunarity estimator based on the triangular prism method and the gliding-box algorithm was developed and found better than existing gray-scale estimators for classifying land-use/land-cover from IKONOS imagery. The accuracy of fractal dimension-aided classification was less sensitive to window size than lacunarity and Moran’s I. In general, the optimal window size for the texture vector-aided approach is 27x27 to 37x37 pixels (i.e., 108x108 to 148x148 meters). As expected, a texture vector-aided approach yielded 2-16% better accuracy than individual textural index-aided approach. Compared to the per-pixel maximum likelihood classification, the proposed genetic Bayesian classifier yielded 12% accuracy improvement by optimizing prior probabilities with the genetic algorithm; whereas the integrated approach with a texture vector and the genetic Bayesian classifier significantly improved classification accuracy by 17-21%. Compared to the neural network classifier and genetic algorithm-support vector machines, the genetic Bayesian classifier was slightly less accurate but more computationally efficient and required less human supervision. This research not only develops a new approach of integrating texture analysis with artificial intelligence for classification, but also reveals a promising avenue of using advanced texture analysis and classification methods to associate socioeconomic statuses with remote sensing image textures

Evaluation of the impacts of Hurricane Hugo on the land cover of Francis Marion National Forest, South Carolina using remote sensing

Hurricane Hugo struck the South Carolina coast on the night of September 21, 1989 at Sullivan’s Island, where it was considered a Category 4 on the Saffir-Simpson scale when the hurricane made landfall (Hook et al. 1991). It is probably amongst the most studied and documented hurricanes in the United States (USDA Southern Research Station Publication 1996). There has been a Landsat TM based Hugo damage assessment study conducted by Cablk et al. (1994) in the Hobcaw barony forest. This study attempted to assess for a different and smaller study area near the Wambaw and Coffee creek swamp. The main objective of this study was to compare the results of the traditional post-classification method and the triangular prism fractal method (TPSA hereafter, a spatial method) for change detection using Landsat TM data for the Francis Marion National Forest (FMNF hereafter) before and after Hurricane Hugo’s landfall (in 1987 and 1989). Additional methods considered for comparison were the principal component analysis (PCA hereafter), and tasseled cap transform (TCT hereafter). Classification accuracy was estimated at 81.44% and 85.71% for the hurricane images with 4 classes: water, woody wetland, forest and a combined cultivated row crops/transitional barren class. Post-classification was successful in identifying the Wambaw swamp, Coffee creek swamp, and the Little Wambaw wilderness as having a gain in homogeneity. It was the only method along with the local fractal method, which gave the percentage of changed land cover areas. Visual comparison of the PCA and TCT images show the dominant land cover changes in the study area with the TCT in general better able to identify the features in all their transformed three bands. The post-classification method, PCA, and the TCT brightness and greenness bands did not report increase in heterogeneity, but were successful in reporting gain in homogeneity. The local fractal TPSA method of a 17x17 moving window with five arithmetic steps was found to have the best visual representation of the textural patterns in the study area. The local fractal TPSA method was successful in identifying land cover areas as having the largest heterogeneity increase (a positive change in fractal dimension difference values) and largest homogeneity increase (a negative change in fractal dimension difference values). The woody wetland class was found to have the biggest increase in homogeneity and the forest class as having the biggest increase in heterogeneity, in addition to identifying the three swamp areas as having an overall increased homogeneity

Wavelet Analysis and Classification of Urban Environment Using High -Resolution Multispectral Image Data.

Attempts to analyze urban features and classify land use and land cover directly from high-resolution satellite data with traditional computer classification techniques have proven to be inefficient. The fundamental problem usually found in identifying urban land cover types from high-resolution satellite imagery is that urban areas are composed of diverse materials (metal, glass, concrete, asphalt, plastic, trees, soil, etc.). These materials, each of which may have completely different spectral characteristics, are combined in complex ways by human beings. Hence, each urban land cover type may contain several different objects with different reflectance values. Noisy appearance with lots of edges, and the complex nature of these images, inhibit accurate interpretation of urban features. Traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture features play an important role in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. This study analyzed urban texture features in multi-spectral image data. Recent development in the mathematical theory of wavelet transform has received overwhelming attention by the image analysts. An evaluation of the ability of wavelet transform and other texture analysis algorithms in urban feature extraction and classification was performed in this study. Advanced Thermal Land Application Sensor (ATLAS) image data at 2.5 m spatial resolution acquired with 15 channel (0.45 mum--12.2 mum) were used for this research. The data were collected by a NASA Stennis LearJet 23 flying at 6600 feet over Baton Rouge, Louisiana, on May 7, 1999. The algorithms examined were the wavelet transforms, spatial co-occurrence matrix, fractal analysis, and spatial autocorrelation. The performance of the above approaches with the use of different window sizes, different channels, and different feature measures were investigated. Six types of urban land cover features were evaluated. Wavelet transform was found to be far more efficient than other advanced spatial methods. The results of this research indicate that the accuracy of texture analysis in classifying urban features in fine resolution image data could be significantly improved with the use of wavelet transform approach

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT metho

Land -Cover Change Detection for the Tropics Using Remote Sensing and Geographic Information Systems.

Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui, Costa Rica. Performances of selected image-processing methods to detect and identify land-cover changes were evaluated. A land-cover time-series from 1960 to 1996 for the site was generated using maps derived from aerial photographs and Landsat-TM classifications. Changes in land-cover from 1986 to 1996 were evaluated using standard landscape indices, and interpreted in terms of their historical context. Dominant changes in the site during this decade included the breakup of extensive cattle ranches for large-scale plantation enterprises and small-scale farming. Colonization processes, improvements in access, and changes in export markets were identified as the major driving forces of change. Evaluation of change-detection methods revealed that postclassification comparison performed significantly better than image differencing algorithms. Image differencing using mid- infrared bonds performed the best of the differencing algorithms tested. Selection of a suitable change-detection method can be aided through examination of the individual bond statistics for the specific area and problem in question. The univariate bond differencing technique has potential for identification of \u27hot spots\u27 of change using Landsat-TM data. Spatial pattern-recognition techniques to characterize complexity of Landsat-TM data were evaluated. Fractal dimension calculated using the triangular prism surface area method, and Moran\u27s I index of spatial autocorrelation, clearly distinguished different land-cover types. Shannon\u27s diversity index and the contagion metric were not found to be useful in characterizing the images. The use of fractal dimension, in conjunction with standard non-spatial descriptive band statistics, are seen as having great potential in characterizing unclassified remotely sensed data based on differences in land-cover types. These statistics could be further developed for rapid environmental monitoring

EMPREGO DA DIMENSÃO FRACTAL PARA SEPARAR CLASSES DE TEXTURA PRESENTES NUMA AEROFOTO DA CIDADE DE PORTO ALEGRE

Neste estudo, é investigada a utilização de uma medida chamada dimensão fractal para fins de classificação de imagens digitais. A dimensão fractal é capaz de quantificar as características espaciais das superfícies geradas a partir das imagens de sensoriamento remoto, especialmente a textura destas superfícies. Os valores de dimensão fractal são calculados pixel a pixel, segundo o método dos Prismas Triangu-lares e posteriormente organizados num formato matricial ou raster, em uma estrutura similar a uma ima-gem digital, podendo ser denominados de bandas fractais. Estas bandas fractais podem ser utilizadas de forma semelhante às tradicionais bandas espectrais em classificadores convencionais. Esta hipótese foi testada com uma aerofoto digitalizada da cidade de Porto Alegre - RS. Observou-se que as imagens fractais, embora apresentem qualidade visual inferior, proporcionam maior separabilidade entre as classes presentes e possibilitam a obtenção de índices de acerto maiores nas classificações, quando comparadas com a imagem espectral. USE OF FRACTAL DIMENSION TO SEPARATE TEXTURAL CLASSES IN AN AERIAL PHOTOGRAPH OF THE CITY OF PORTO ALEGRE Abstract Interest is currently growing in the use of spatial attributes for automatic classification of digital images, as is clearly demonstrated by the increasing number of scientific papers on the topic. The reason for this interest is that some classes in natural scenes are not easily distinguished by the spectral features (urban areas, for instance). Urban areas, in particular, are better defined by spatial attributes, such as texture. This research explores the use of fractal dimension to characterize and separate textural classes present in an aerial photograph of Porto Alegre, capital city of the State of Rio Grande do Sul, Brazil. The fractal dimension can be considered as a measure of the spatial complexity of surfaces generated from remotely-sensed images and it is calculated here over moving windows with 7x7 and 9x9 pixels, using the Triangular Prism method. By using a moving window, it was possible to organise the data in a format similar to that used in spectral bands, thus obtaining fractal-dimension bands, which were converted to digital counter values (between 0 and 255). The Bhattacharya distance was used to estimate the separability between pairs of classes, and Gaussian maximum likelihood was used to classify pixels in the images composed of both fractal and spectral bands. The stronger differentiation between classes, together with the high percentage of successes in test samples, shows that the fractal approach can be useful in automatic classification procedures and in situations where the spectral information alone is not sufficient to distinguish the classes successfully

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 2: TIMS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; the summaries for this workshop appear in Volume 1; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; the summaries for this workshop appear in Volume 2; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5; the summaries for this workshop appear in Volume 3

Characterization of Forested Landscapes from Remotely Sensed Data Using Fractals and Spatial Autocorrelation

The characterization of forested landscapes is frequently required in civil engineering practice. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions (FDs), along with a simple spatial autocorrelation technique (Moran’s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results indicate that when smaller trees do not dominate the landscape (<50%), forested areas can be differentiated according to breast sizes and thus important flood plain characteristics such as ratio of obstructed area to total area can be estimated from remotely sensed data using the studied indices. This would facilitate the estimation of hydraulic roughness coefficients for computation of flood profiles needed for bridge design. FD and Moran’s I remained fairly constant around the values of 2.7 and 0.9 (resp.) for samples with either greater than 50% saplings or less than 50% sawtimber and with ranges of 2.7–2.9 and 0.6–0.9 as the saplings decreased or the sawtimber increased. Those indices can also distinguish hardwood and softwood species facilitating forested landscapes mapping for preliminary environmental impact analysis