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The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

Space-Earth Integrated Stereoscopic Mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining deep learning and remote sensing images for the classification of land use scenes. However, improvement of classification accuracy has been limited because of the lack of sectional representation. Landscape photos can describe the crosssections in detail. For this reason, this study constructed a Landuse Semantic Photo Dataset (LSPD) and proposed a Land-use Classification Framework for Photos (LUCFP) based on Inception-v4. LSPD was constructed through semantic planning, scene segmentation, supervised iteration transfer learning and augmentation of photos. LSPD has 1.4 million photos collected from seven geographic regions of China, and covers 13 land-use categories and 44 semantic categories. LUCFP adapts scene segmentation based on depth of field, multi-semantic block labeling, and weighting of semantic joint spatial ranges to determine the land use category. To validate LUCFP, nine semantic samples (9×3×2000 photos) were chosen from LSPD, obtaining an overall accuracy of 97.64%. The best photo cropping method was masking, which crops the boundary of the scene labeled by the photo, leading to an accuracy of 90.32%. The optimal pixel size that balances speed and accuracy is 675×675, with speed reaching 30 photos per second with an average accuracy of 93.80%. LUCFP has been successfully applied to the automatic verification of land surveys in China. Index Terms-deep convolutional neural networks (DCNN), landscape photos, land survey, land use scene classification I. INTRODUCTION and use classification is essential for applications such as land resource management, urban planning, precision agriculture and environmental protection [1, 2], whose essence is to classify the images (remote sensing images or ground-level landscape photos) that reflect the present situations of land use.

Reversible data hiding in encrypted image (RDHEI) is a technique that can provide the security and invisibility of their own information during the acquisition and sharing of them among multi-users. Among which, parametric binary tree labelling (PBTL) is a novel technique designed to serve for highcapacity RDHEI. However, considering the local smoothness of the image, its potential redundancy room has not been fully explored. In this paper, we propose an improved PBTL-RDHEI scheme (IPBTL-RDHEI in short). In IPBTL-RDHEI, an adjusting pixel modulation strategy is considered to reduce the probability of the overflow of pixels, making more embeddable pixels usage to carry secret data. Moreover, a blockwise rearrangement and compression (BRC) mechanism is introduced to decrease the length of the required auxiliary information. Experimental results confirm that the proposed scheme is able to achieve an average embedding rate as large as 1.827 bpp when the block size is set to 3×3. INDEX TERMS IPBTL-RDHEI, adjusting pixel modulation, block-wise rearrangement and compression (BRC), embedding rate.

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