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: 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: 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: 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: 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: Some performance evaluation systems for building extraction techniques are manual in the sense that only visual results are provided or human judgment is employed. Many evaluation systems that employ one or more thresholds to ascertain whether an extracted building or roof plane is correct are subjective and cannot be applied in general. There are only a small number of automatic and threshold-free evaluation systems, but these do not necessarily consider all special cases, e.g., when over- and under-segmentation occurs during the extraction of roof planes. This paper proposes an automatic and threshold-free evaluation system that offers robust object-based evaluation of building extraction techniques. It makes one-to-one correspondences between extracted and reference entities using the maximum overlaps. Its application to the evaluation of a building extraction technique shows that it estimates different performance indicators including segmentation errors. Consequently, it can be employed for bias-free evaluation of other techniques whose outputs consist of polygonal entities.

Description: A fully polarimetric synthetic aperture radar (PolSAR) image allows the generation of a number of polarimetric descriptors. These descriptors are sensitive to changes in land use and cover. Thus, the objective of this study is twofold: first, to identify the most effective descriptors for each change type and ascertain the best complementary pairs from the selected polarimetric descriptors; and second, to develop an information fusion approach to use the unique features found in each polarimetric descriptor to obtain a better change map for urban and suburban environments. The effectiveness of each descriptor was assessed through statistical analysis of the sensitivity index in selected areas and through change detection results obtained by using the supervised thresholding method. A good agreement was found between the statistical analysis and the performance of each descriptor. Finally, a polarimetric information fusion method based on the coupling of modified thresholding with a region-growing algorithm was implemented for the identified complementary descriptor pairs. The mapping accuracy, as measured by the Kappa coefficient, was improved by 0.09 (from 0.76 to 0.85) with a significant reduction of false and missing alarm rates compared to using single PolSAR images.

Description: In China, rapid urbanization has increased the demand for urban land and intensified the conflict between limited land resources and urban development. In response, high urban density has been proposed to realize sustainable urban development. Achieving this goal requires an examination of the dynamics of urban density in China. Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are a good indicator of human activity. We applied NTL data to measure urban density in 70 major cities in China during 1992–2010. Based on temporal changes in NTL, we identified seven classes of urban density and clustered the distributions of urban density in 70 cities into six types. The dynamics of urban density were then obtained from the GDP density as an index of city development. The curves of urban density distribution gradually changed from a concave increase to W-shaped and S-shaped to a concave decrease, indicating that the current urban land use in China is unsustainable and that the shortage of land resources must be addressed. An examination of the distribution of urban density in Hong Kong revealed a different pattern and a potential solution for cities in mainland China.

Description: Water is a fundamental element in urban environments, and water body extraction is important for landscape and urban planning. Remote sensing has increasingly been used for water body extraction; however, in urban environments, this kind of approaches is challenging because of the significant within-class spectral variance in water areas and the presence of complex ground features. The objective of this study is to develop an automatic method that could improve water body extraction in urban environments from moderate spatial resolution satellite images. Central to our method is the combined use of multiscale extractions and spectral mixture analysis techniques in adaptive local regions. Specifically, we first calculate the NDWI image from experimental images for selecting water sample pixels. Second, on the basis of the selected water pixels, we apply an improved spectral mixture analysis technique on the experimental image to get water abundance of every pixel, and segment the abundance image to extract water bodies at the global scale. Third, in a similar manner, we iteratively conduct the water body extraction in multiscale local regions to refine the water bodies. Finally, the final result of water bodies is obtained when a stopping criterion is satisfied. We have implemented this method to produce water maps from an ALOS/AVNIR-2 image and a Terra/ASTER image covering urban areas. The experimental results illustrate that the proposed method has substantially outperformed two related methods that use the NDWI-based thresholding and the SVM classification for the entire image.