No abstract

Abstract:Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler's First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

This letter presents a novel spatial feature called object correlative index (OCI) to enhance the classification of very high resolution images. This novel method considers the property of an image object based on spectral similarity to construct a useful OCI to describe the spatial information objectively. Compared with the generic features widely used in image classification, the classification approach based on the OCI spatial feature results in higher classification accuracy than those approaches that only consider spectral features or pixelwise spatial features, such as the pixel shape index and mathematical morphology profiles. Experiments are conducted on QuickBird satellite image and aerial photo data, and results confirm that the proposed method is feasible and effective.Index Terms-Classification of very high resolution (VHR) image, object correlative index (OCI), spatial feature, spectral feature.

No abstract

The development of p-type metal oxide thin-film transistors (TFTs) is far behind the n-type counterparts. Here, p-type CuAlO2 thin films were deposited by spin coating and annealed in nitrogen atmosphere at different temperature. The effect of post-annealing temperature on the microstructure, chemical compositions, morphology, and optical properties of the thin films was investigated systematically. The phase conversion from a mixture of CuAl2O4 and CuO to nanocrystalline CuAlO2 was achieved when annealing temperature was higher than 900 °C, as well as the transmittance, optical energy band gap, grain size, and surface roughness of the films increase with the increase of annealing temperature. Next, bottom-gate p-type TFTs with CuAlO2 channel layer were fabricated on SiO2/Si substrate. It was found that the TFT performance was strongly dependent on the physical properties and the chemical composition of channel layer. The optimized nanocrystalline CuAlO2 TFT exhibits a threshold voltage of − 1.3 V, a mobility of ~ 0.1 cm2 V−1 s−1, and a current on/off ratio of ~ 103. This report on solution-processed p-type CuAlO2 TFTs represents a significant progress towards low-cost complementary metal oxide semiconductor logic circuits.