基于JPEG2000压缩域的油库检测

油库检测是遥感图像目标检测领域的一个研究热点. 针对目前遥感图像通常以JPEG2000压缩格式储存的现状,提出基于JPEG2000的光学遥感图像油库检测算法. 在不经过全部解压的数据中,直接提取小波系数进行目标的检测. 根据小波系数的不同特性,针对低频子带,采用Hough变换提取油库的圆形形状特征;针对高频子带,利用堆叠降噪自编码器进行特征提取和描述. 最后利用支持向量机进行特征融合和目标检测. 实验结果表明,本算法能够准确快速地检测遥感图像中的油库目标,具有较高的检测率和较快的处理速度. 

Oil Depot Detection Based on JPEG2000 Compression Domain

Detecting oil depot has been a hot research field in object detection from remote sensing images. As the images usually are storage by JPEG2000, a oil depot detection algorithm was proposed. With only part data decompression, the object could be detected directly by the wavelet coefficients. According to the different frequency of wavelet coefficients, Hough transform was used to extract circle feature in low-frequency subband and stacked denoising autoencoders could be used to represent the feature of oil depot in high-frequency subband. At last, support vector machine was utilized to make feature fusion and object classification. Many experiments indicate that the proposed algorithm can detect oil depot with high accuracy as well as fast processing speed.