[Spectral curve shape feature-based hyperspectral remote sensing image retrieval].

With the rapid development of technology of sensors and data transmission, using all kinds of airplane sensors and satellite sensors, the authors can get different voluminous remote sensing image data of earth. Those voluminous remote sensing image data bring problems of data storage and management. It is becoming increasingly necessary to retrieve some information the authors need from those voluminous image data. Image retrieval was proposed by CHANG firstly in 1980 and can be regarded as expansion of traditional information retrieval. Oriented to the demands of efficient retrieval for voluminous remote sensing image, and considering that there are many bands in hyperspectral remote sensing image, the authors first analyzed image distance function and similarity measure in image retrieval. The most crucial issues in retrieval are spectral features extraction and similarity measure. In the present paper, the authors used classical Douglas-Peucker algorithm (hereinafter referred to DP algorithm) for curve simplification to extract shape features of spectral curve, in order to speed up hyperspectral remote sensing image retrieval. And the authors proposed a new method of spectral curve and remote sensing image retrieval, called Douglas-Peucker Spectral Retrieval algorithm (hereinafter referred to DPSR algorithm). Spectral shape features were used in image retrieval. DPSR used features of spectral curve, reduced the computation amount, realized match and retrieval efficiently, and is suitable for spectral curve retrieval in hyperspectral remote sensing image. The authors selected four ground features (grass, apple garden, grape garden and pond) in OMISI hyperspectral remote sensing image to compute similarity measure results, in order to test the effect of DPSR algorithm. Compared with traditional analysis method such as spectral angle match (SAM) and spectral information divergence (SID), DPSR can maintain high precision of results with less amount of computation, and then a new efficient image spectral retrieval method is provided. Besides some additional work the authors need to do in the future, for example, the relationship between threshold, retrieval precision rate and retrieval speed.