<p class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;"><span style="mso-bidi-font-weight: bold;">A common drawback of the image sharing with steganography approaches is that the revealed secret image is distorted due to the truncation of the grayscale secret image. To lossless reveal the secret image in the (<em>t</em>, <em>n</em>)- threshold, we provide a novel sharing scheme in this article. Moreover, the original host image can be recovered by the embedded shadow images. To accomplish the above purposes, the proposed scheme derives the secret shadows and generates the meaningful shadow images by adopting the sudoku. In the new scheme, the sudoku grid is setting to 16</span><span style="mso-fareast-font-family: SymbolMT;"></span><span style="mso-bidi-font-weight: bold;">16 and divided into sixteen 4</span><span style="mso-fareast-font-family: SymbolMT;"></span><span style="mso-bidi-font-weight: bold;">4 blocks. Thus, we can embed 4</span><span style="mso-fareast-font-family: SymbolMT;"></span><span style="mso-bidi-font-weight: bold;">(<em>t</em>-1) secret bits into each pixel pair of the host image. Besides, the embeddable secret capacity can be improved according to the threshold <em>t </em>in the (<em>t</em>, <em>n</em>)-threshold sharing system. The experiments show that the shadows can be successfully camouflaged in the host image with satisfactory quality. The distortion of the embedded host pixels is limited within range [0, 3]. Moreover, the proposed scheme provides a large capacity for embedded secret data. </span></span></span></p>
With double-temporal Landsat TM and ETM+ datasets, the change information of forest resources of Culai Mountain in Shandong Province was explored. This paper applies decision tree classification based on C5.0 algorithm and neighborhood correlation image analysis to detect forest change information,and compares the three different detection methods:1)C5.0 classifies single-temporal data respectively,and extract change information after comparing classification results;2) create C5.0 train rules through double-temporal raw data,then generate change detection map;3)In addition to double-temporal remote sensing data,neighborhood correlation analysis images are also added as one of the data sources of C5.0,and generate change detection map. The experimental result shows that decision tree classification based on C5.0 algorithm can detect change information effectively,and after adding neighborhood correlation analysis images the classification accuracy of change detection was improved.
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