为了探究适合国产高分卫星影像的融合方法,该文以国产亚米级高分辨率卫星BJ-2和GF-2影像为数据源,分别选取像素级影像融合方法中较为典型的GS、Pansharp、NND、HPF、PCA和PCA+Wavelet对2种影像进行处理,并采用定性和定量分析法对其融合效果进行评价.结果表明:对于BJ-2影像,Pansharp和GS的融合效果相对较好,光谱保真度较佳、清晰度较高、光谱扭曲度较小;对于GF-2影像,Pansharp融合效果相对较好,HPF次之,光谱保真度较佳、清晰度较高、光谱扭曲度较小;对于2种影像,PCA融合法的效果最差.

针对影像分割中的尺度选取问题,该文基于GF-2多光谱数据提出一种基于自上而下自适应分割尺度的分类方法,该方法在提取每一分割对象光谱、纹理特征的基础上,构建其在各波段复杂度函数,根据每类地物在各波段的复杂度阈值和分类规则,经迭代计算,确定每一对象的最适宜尺度和所属地类,进而得到具有最佳尺度的分割和分类结果.将其与采用ESP尺度分析算法得到的单一最优尺度下的分类结果进行对比分析,结果表明:该方法能够获取与地面目标相匹配的分割尺度,改善了分割效果,提高了分类精度,具有一定实用价值.

With the successful launch of China's GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolution remote sensing satellite is fused by PCA, Pansharp, Gram-Schmidt and NNDiffuse fusion. Then, the quality of the fusion images were evaluated subjectively and objectively. In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. The results showed that: (1) The best visual effect of GF-2 fusion image was the Pansharp fusion image; The quantitative evaluation results showed that the brightness and information retention of Gram-Schmidt fusion image was the best, while the Pansharp fusion image had the highest correlation with the original multi-spectral image; the NNDiffuse fusion image had the highest clarity, and the PCA fusion image quantitative evaluation effect was the worst; (2) According to the applicability analysis of the fusion images based on different classification algorithms with features information extraction, it could be seen that the NNDiffuse fusion method was used for the fusion of GF-2 image data, and the classification of the fusion images was more suitable by using KNN or Random Trees classification algorithm.

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran's I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.

Multiscale segmentation is the premise and key step of geographic object-based image analysis (GEOBIA), but scale selection remains a challenge in multiscale segmentation. Over the years, scale selection and evaluation in image segmentation has been extensively explored and many methods have been developed. In these methods, when a scale is chosen or evaluated, all the features are generally extracted from images. In addition, an optimal scale is generally selected based on the pre-estimation of the statistical variance in remote sensing images or determined based on the postsegmentation evaluation of segmented results. In this study, a method was proposed to identify the optimal scale of each segmented object during the segmentation through combining the a priori thematic map knowledge with image features. First, 25 image segmentations were obtained using multiresolution segmentation algorithm of Definiens Professional 9.0 with different scales. A global score (GS) value was assigned to each segmentation based on the calculation results of the weighted variance and global Moran's I and the single-scale optimal segmentation result was determined according to each GS of 25 segmentation scales. Second, the image feature complexity information and the a priori thematic map complexity information of each segmentation object were extracted to calculate complexity values for each object. Third, the optimal scale of each segmentation object was determined through the iterative calculation with the multithreshold method. Finally, the segmentation results of the proposed method were evaluated. The proposed method was applied to process Gaofen-2 (GF-2), GF-1, Korea Multipurpose Satellite (KOMPSAT-2), IKONOS, QuickBird and WorldView-2 high resolution satellite images to obtain the segmentation results and classification results, compared with results obtained of the optimal singlescale segmentation and the unsupervised evaluation method. The experimental results of GEOBIA showed that the method was helpful for generating the segmentation object with the optimal scale. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

In this paper, it selects two phases TM data of Wenchuan County on Sept.18, 2007 and July 18, 2008. The pixel decomposition of the two images before and after the earthquake was obtained to extract vegetation covering information using linear spectral un-mixing method (LSNLM). According to the vegetation growth characteristics in the studied area the vegetation coverage is divided into 5 grades: high vegetation cover, high or middle vegetation cover, middle vegetation cover, low or middle vegetation cover and low vegetation cover. Through comparative analysis before and after the earthquake, the earthquake and earthquake-induced secondary collapse, landslide and debris flow and other geological disasters have directly led to different degrees changes of vegetation coverage.