Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image coul...
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Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatialinformation of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.
Land use and land cover change (LUCC) is necessary to explore the factors leading to heavy drought and rainy-flood disaster in some districts of Sichuan province. A method based RS, GIS, GPS and Google earth (GE) is p...
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Land use and land cover change (LUCC) is necessary to explore the factors leading to heavy drought and rainy-flood disaster in some districts of Sichuan province. A method based RS, GIS, GPS and Google earth (GE) is presented to establish LUCC database in Sichuan province and Chengdu district. At first, LUCC is interpreted based on the new temporal images and the land use and land cover database from TM in ***, some ground objects, which could not be identified in the new temporal images, were interpreted utilizing GE with some higher spatial resolution images. Thirdly, the new interpreted LUCC was validated in the field with GPS handheld receiver. Then, LUCC of Sichuan province was updated. A comparative analysis of LUCC between in Sichuan province and in Chengdu district was conducted and the result showed: (1) a large amount of farmland in Sichuan Province was occupied from 2000 to 2005 and the area is 84 573 ha. While construction land gained obviously and the area was 35 828 ha. The dynamic degree of construction land was 111.10 0 / 00 from 2000 to 2005. The LUCC demonstrated that the economy of Sichuan province continued to develop, the cities were overspreading and the urban heat island effect was deteriorated from 2000 to 2005. (2) A large amount of farmland was also occupied in Chengdu district from 2000 to 2005, the area amounted to 12 989 ha. The farmland lost was mainly changed to construction land, amounting to 93%. And the dynamic degree was 117.41 0 / 00 from 2000 to 2005, which was bigger than that in Sichuan province.
Stereo dense image matching (DIM) is a key technique in generating dense 3D point clouds at low cost, among which semi-global matching (SGM) is one of the best compromise between the matching accuracy and the time cos...
Stereo dense image matching (DIM) is a key technique in generating dense 3D point clouds at low cost, among which semi-global matching (SGM) is one of the best compromise between the matching accuracy and the time cost. Most commercial or open-source DIM software packages therefore adopt SGM as the core algorithm for the 3D point generation, which computes matching results in 2D image space by simply aggregating the matching results of multi-directional 1D paths. However, such aggregations of SGM did not consider the disparity consistency between adjacent pixels in 2D image space, which will finally decrease the matching accuracy. To achieve higher-accuracy while keep the high time efficiency of SGM, this paper proposes an improved SGM with a novel matching aggregation optimization constraint. The core algorithm formulates the matching aggregation as the optimization of a global energy function, and a local solution of the energy function is utilized to impose the disparity consistency between adjacent pixels, which is capable of removing noises in the matching aggregation results and increasing the final matching accuracy at low time cost. Experiments on aerial image dataset show that the proposed method outperformed the traditional SGM method and another improved SGM method. Compared with the traditional SGM, our proposed method can increase the average matching accuracy by at most 11%. Therefore, our proposed method can applied in some smart 3D applications, e.g. 3D change detection, city-scale reconstruction, and global survey mapping.
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