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The near-equatorial orbit (NEqO) image bands have large differences in distortion, scaling, and skewing. Therefore band-to-band registration of a near-equatorial orbit image is challenging. This study thus proposes a technique to overcome the band-to-band registration of NEqO image bands. The proposed method involves the conversion of image bands into greyscale, followed by image compression and automatic control point (CP) generation through scale-invariant feature transform. CPs are used to perform registration with both first- and second-order polynomials and spline transformation to correct the misregistration between NEqO image bands. The images used in this study are from the Malaysian RazakSAT satellite. Results show accurate registration performance. Moreover, the root-mean-square errors of the first- and second-order polynomial transformations are 4 and 3 m, respectively. These values are lower than those obtained with the use of spline transformation. Verification is performed by comparing the results of both automatic and manual transformations to the use of polynomial transformation. The results confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials. [ABSTRACT FROM AUTHOR]