Description: This paper investigates the potential effects of spectral, shape, textural, and height information and their combinations on the classification of urban areas using airborne hyperspectral data. Based on analysis of the spectral, shape, textural, and height characteristics of urban land covers, the first ten spectral principal components, eight shape components, one height component, and seven textural components were selected to examine their performance on the classification accuracy. Correlation analysis was conducted to exclude correlated components. A support vector machine (SVM) was employed to determine the significant components affecting the urban hyperspectral classification through comparison of the classification accuracy. Different combinations of these components were then tested to estimate their contributes. The classification results showed that all these components contribute to the result of urban land cover classification, but different land cover classes benefit from the inclusion of different components. The experiment further revealed the effect of significant components on the classification of urban land cover in terms of area, convexity, elongation, form factor, rectangular fit, roundness, textual factors, and mean relative height. It is suggested that the inclusion of shape, texture, and height, together with the spectral components, significantly improved the classification accuracy of urban land cover.

Description: This paper attempted to evaluate the effects of urban growth on the land surface temperature (LST) in Beijing, China. Firstly, the multi-temporal land use types were derived based on the images of Landsat 5 TM and Landsat 7 ETM+ from 1996 to 2010, and two types of land use change trajectories (existing urban trajectory and urban growth trajectory) were also established to category the land transformation type under urbanization. Then the algorithm developed by the Landsat project Science Office was adopted to retrieve LST, in which the digital number (DN) of Band6 was converted to the spectral radiance, followed by being converted to the brightness temperature under the assumption of unity emissivity, and corrected for emissivity $(varepsilon)$ to obtain the natural surface temperature. Finally, to evaluate the effects of urban growth on the LST change, two indices of deviation degree and LST change contribution were built and calculated for each trajectory. The results demonstrated that the average deviation degree of urban growth trajectories was much greater than that of the existing urban trajectory, which presented a more violent LST change. However, the effects of urban growth on the LST change of the study area were very limited because the smaller areas of the urban growth trajectories would generate a limited LST change contributions. The results could not only provide a new way and methodology to assess the relationship between urban growth and the LST change, but also support the management and planning in the urban area.

Description: Research in change detection from optical satellite data is widely investigated as a support for visual image analysis. Most of the methods, however, are based on radiometric changes and are suffering from high false alarms rate due to irrelevant radiometric changes. Change detection based on the elevation difference between two dates, therefore, seems a good alternative to identify relevant changes, especially in a context of urban change detection. In the present work, we provide a fully automatic method of change detection based on a digital surface model (DSM) comparison. The processing flow includes the bundle block adjustment of all the available data as a preprocessing step, followed by an improved DSM generation scheme and a differential DSM analysis. The last two steps have been formulated as labeling problems and solved by an optimization method with a spatial regularization constraint. The solution of these labeling problems is computed with a generalized dynamic programming algorithm that is adapted according to the input data and the defined labels. The final DSMs reach a planimetric and altimetric resolution of about 1 m, allowing changes from $bf 20nbsphbox{m}^{2}$ to be detected. The results show that 33%–75% (respectively about 95%) of all changes (respectively, changes larger than $bf 100nbsphbox{m}^{2}$ ) are detected, depending on the employed regularization and the area. Moreover, the calculated kappa coefficient of the processing flow reaches up to 0.80, which emphasizes the method accuracy. All the above features lead to a significant gain compared to the classical visual image analysis.

Description: The seasonal and spatial variability of surface heat fluxes is crucial to the understanding of urban heat island phenomenon and dynamics. To estimate energy fluxes, remote sensing derived biophysical variables need to be integrated with surface atmospheric parameters measured in meteorological stations or in situ field measurements. In this study, based on the two-source energy balance algorithm, we applied a method to estimate surface energy fluxes by combined use of multispectral ASTER images and routine meteorological data, and applied it to the City of Indianapolis, United States, aiming at in-depth understanding of the spatial patterns of energy fluxes. By computing the fluxes by land use and land cover (LULC) type, we further investigated the spatial variability of heat fluxes. Results show that the energy fluxes possessed a strong seasonality, with the highest net radiation in summer, followed by spring, fall and winter. Sensible heat flux tended to change largely with surface temperature, while latent heat was largely modulated by the change in vegetation abundance and vigor and the accompanying moisture condition. The fluctuation in all heat fluxes tended to be high in the summer months and low in the winter months. Sensible and latent heat fluxes showed a stronger spatial variability than net radiation and ground heat flux. The variations of net radiation among the land use/cover types were mainly attributable to surface albedo and temperature, while the within-class variability in the turbulent heat fluxes was more associated with the changes in vegetation, water bodies, and other surface factors.

Description: Building extraction is one of the primary applications of urban remote sensing. Polarimetric synthetic aperture radar (POLSAR), with its all-weather day and night imaging, canopy penetration and full polarimetric information, provides a unique way to detecting and characterizing urban areas. In this study, the time-frequency decomposition technique and the entropy/alpha-Wishart classifier were integrated to improve building extraction. The entropy/alpha-Wishart classifier was able to extract ortho-oriented buildings. After time-frequency transformation, the variation of entropy, alpha, anisotropy differs for objects with different scattering mechanisms, and the alpha angle of subaperture images was optimal in delineating slant-oriented buildings. A comparison between the integrated approach and the conventional entropy/alpha-Wishart classifier was performed on both C- and L-band NASA/JPL AIRSAR datasets. The overall accuracy and kappa value of the integrated approach was about 20% higher than that of the entropy/alpha-Wishart classifier. The C-band output tends to show more detailed scattering properties whereas the extracted buildings from the L-band image reveal better overall visual results.

Description: Local governments in Japan need to assess landscape quality from the perspective of aesthetics before approving new construction. However, ground surveys are time-consuming and expensive to perform over wide areas. This paper proposes a methodology using a digital surface model (DSM) to estimate a local landscape index, referred to as the “enclosure index”, in urban areas. The index is defined as the ratio of occluded area to the entire field of view. The index can be applied to local landscape assessment from the viewpoint of a human, and it is expected to allow for estimating the landscape quality over a wide area at low cost. The index is defined in two different projections, and two types of DSMs for calculating the index were investigated: DSM from airborne LiDAR (LiDAR-DSM) and DSM generated from stereoscopic aerial images (Image-DSM). Enclosure index (occlusion) maps were generated, and the indices were calculated in the study area of Higashiyama, Kyoto, Japan. As a result of validation based on ground truth data, it was found that the index from LiDAR-DSM was more accurate than that from Image-DSM because the height accuracy of LiDAR-DSM was higher than that of Image-DSM, especially for narrow streets, where three-dimensional coordinates are difficult to restore by using aerial images. Even though last-pulse mode LiDAR data may yield underestimated DSM, it was found that the proposed methodology is effective for estimating the index over a wide area at low cost.

Description: The morphological hit-or-miss transform (HMT) was found to be efficient for the detection of buildings in panchromatic bands of very high spatial resolution images. The use of multispectral information was judged to be necessary to improve the results. The application of morphological operators to multispectral images is problematic, as no universal strategy for ordering the multivalued pixels of these images has been widely adopted. In this paper, we propose a new method to detect building locations based on a recently developed concept for the HMT to handle noise, called percentage occupancy HMT (POHMT). The parameters for the POHMT were defined with the aid of the top-hat by reconstruction transformation. To eliminate irrelevant locations, we applied a vegetation mask and verified locations by their proximity to shadows. The novelty of the method consists in the proposed vector-based strategy that allows for the application of the POHMT to multispectral images in order to detect building locations. Moreover, an original technique to automatically define the parameters for the POHMT was proposed. The method was tested on subsets from a pan-sharpened Ikonos image and from raw GeoEye and WorldView-2 images. The experimental results are promising.

Description: In this paper, extensive real-data experiments for the investigation of the benefit of exploiting multiple aspects and multiple baselines for the reconstruction of urban surface models by synthetic aperture radar interferometry are documented. These experiments are carried out within a recently proposed reconstruction framework that allows the fusion of almost arbitrary configurations of multi-aspect multi-baseline InSAR data. The results based on airborne decimeter-resolution millimeterwave imagery prove and quantify that multiple baselines help to solve the phase ambiguity problem, while multiple aspects reduce the parts of the scene affected by radar shadowing. In addition, the inherent redundancy provides a significant improvement in the achievable reconstruction accuracy, which is evaluated relative to the reconstruction error common for conventional single-aspect single-baseline SAR interferometry.

Description: China has experienced a rapid urban expansion over the past three decades because of its accelerated economic growth. In this study, we detected and analyzed the urban expansion of China during this period using multi-temporal Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and multi-source Normalized Difference Vegetation Index (NDVI) data. First, an intercalibration was performed to improve the continuity and comparability of the nighttime light data from 1992 to 2010. The nighttime light and NDVI data were then subjected to a local support vector machine (SVM) based region-growing method to extract the urban areas from 1992 to 2010. The urban areas from 1981 to 1991 were identified using the areas in 1992 and NDVI data, based on the hypothesis that China’s urban expansion continued during this period. Finally, the extracted time-series urban maps were validated with Landsat images. The proposed local SVM-based region-growing method performed better than a local thresholding method and a global SVM-based region-growing method according to visual and quantitative comparisons of the urban boundaries and areas. We also analyzed the expansion rates to understand the dynamics of the urban areas in China and in its seven economic regions. In particular, the urban expansion patterns were investigated in three typical urban agglomerations, i.e., Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta. The proposed urban expansion direction, urban expansion intensity, and relative ratio of urban expansion demonstrated the regional variation among the three urban agglomerations.

Description: This study proposes a multiscale grid method to detect and reconstruct building roofs from airborne LiDAR data. The method interpolates unorganized LiDAR point cloud into two sets of grids with different spatial scales. In the large-scale grid, building seed regions are obtained, including detection of initial building seed regions and refinement of building seed regions. In the small-scale grid, to detect the detailed features of building roofs with complicated top structures, a high-resolution depth image is generated by a new iterative morphological interpolation using gradually increasing scales, and then segmented by using a full $lambdab$ -schedule algorithm. Based on the building seed regions, detailed roof features are detected for each building and 3-D building roof models are then reconstructed according to the elevation of these features. Experiments are analyzed from several aspects: the correctness and completeness, the elevation accuracy of building roof models, and the influence of elevation to 3-D roof reconstruction. The experimental results demonstrate promising correctness, completeness, and elevation accuracy, with a satisfactory 3-D building roof models. The strategy of hierarchical spatial scale (from large scale to small scale) obtains the complementary advantage between technical applicability in a large urban environment and high quality in 3-D reconstruction of building roofs with fine details.