With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road; Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.
Ruoergai Conservation area belongs to the Aba Tibetan and Qiang Autonomous Prefecture, Sichuan Province. In recent decades, the wetland and grassland have degraded very seriously. In this paper, based on GIS spatial analysis techniques, we utilized the Landsat 7 ETM + images in 2007, 2009, and 2011, to extract the land use/land cover change in Ruoergai. Firstly, the images were enhanced, mosaicked, and subset. Secondly, the supervised and unsupervised classification method were used to derive the land use/land cover change information in Ruoergai. Finally, the land use / land cover change in Ruoergai was analyzed between 2007 and 2011 .
Our results were listed below:
(1)The area of water body and swamp in Ruoergai reduced by 52.346% between 2007 and 2011.
(2)The area of grass land in Ruoergai decreased by 12.754% between 2007 and 2011.
(3) The area of woodland in Ruoergai reduced by 3.224% between 2007 and 2011.
(4) The area of bare land, cultivated land and construction land increased by 6.647% between 2007 and 2011.
We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) to discriminate different types of land cover. We evaluated the proposed algorithm on three well-known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit, and image fusion and recursive filtering. Experimental results show that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification.
Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap.
Vehicle detection is a very important task for intelligent transportation system. In this paper, a method with mathematical morphology and template matching is presented to detect the crowded vehicles of parking lot with high resolution aerial image. Our experimental results with high resolution aerial image showed that the graded image, with the spatial resolution of 1×1ft, could greatly reduce the calculation time, but with the same accuracy as the original image with the spatial resolution of 0.5×0.5ft .
A series of environmental policies in Sichuan province was executed to restore the grassland and forestland on some degraded lands after 2000. But the effectiveness on land use and cover change (LUCC) has not yet been systematically investigated. We undertook a detailed analysis about land use and cover change between 2000 and 2005 in Sichuan province. Our study mainly utilized remotely sensed data of 2005 China-Brazil Earth Resources Satellite II (CBERS II) and 2000 Landsat 5 thematic mapper (TM) data. Land use and cover change between 2000 and 2005 was visually interpreted by CBERS II with ArcInfo Workstation based on land use and cover database interpreted from TM. Then LUCC was validated by ground truth with global positioning system receivers. Our analysis illustrates that the conservation policies to restore the grassland and forestland were successful to a lesser extent. But more measures to restore the grassland and forestland of Sichuan province have to be taken further in the future.
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