Fusion of satellite imagery with different spatial resolutions based on statistical techniques

基于统计技术的不同空间分辨率的卫星图像融合

• 批准号: 09640271
• 负责人: NISHII Ryuei
• 金额: $ 1.34万
• 依托单位: HIROSHIMA UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1997
• 资助国家: 日本
• 起止时间: 1997 至 1998
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09640271/
• 关键词: Discriminant analysis; Entropy; Information criterion; Measure of accuracy; Satellite imagery; Variable selection; 高解像度化; 近傍選択

The Landsat TM sensor for thermal infrared measurement has lower spatial resolution than that of other six measurements. Nishii et al (1996) proposed a procedure to correct the infrared measurement by a linear combination of remaining high-resolution measurements. However, the linear combination based on all measurements does not always give a better result. For the selection problem, Nishii, Kusanobu and Nakaoka (1999) take an information theoretic approach (AIC) by Akaike (1973). AIC in our case is reduced to that in canonical correlation analysis which is obtained by Fujikoshi (1985). An averaged AIC is proposed for the problem and the procedure shows a good performance for actual TM images.Our other results related to the satellite data analysis are as follows :- It is explained that the kappa statistic, which is widely used for assessing the accuracy of land-cover classification, is not suitable for the accuracy assessment. The criterion based on Kullback-Leibler divergence is alternately proposed. (Nishii and Tanaka, 1999)- The regression model between forest and population is extended to the spatial nonlinear regression with relief as a new covariate. The new model shows a good fit for an actual data of Hiroshima.(Tanaka and Nishui, 1997)- It is shown that the lung cancer mortality in local governments is significantly related to the distance from the shoreline through spatial nonlinear regression. This suggests that the further epidemiological study is required. (in preparation)- For the denoising purpose, time series data is expanded by the wavelet functions and Bayes model is used for shrinking the wavelet coefficients. The procedure is shown to denoise successfully various numerical examples. (in preparation)

用于热红外测量的 Landsat TM 传感器的空间分辨率低于其他六种测量传感器。 Nishii 等人 (1996) 提出了一种通过剩余高分辨率测量的线性组合来校正红外测量的程序。然而，基于所有测量的线性组合并不总是给出更好的结果。对于选择问题，Nishii、Kusanobu 和 Nakaoka (1999) 采用了 Akaike (1973) 的信息论方法 (AIC)。我们案例中的 AIC 被简化为 Fujikoshi (1985) 获得的典型相关分析中的 AIC。针对该问题提出了平均 AIC，该程序对实际 TM 图像显示出良好的性能。我们与卫星数据分析相关的其他结果如下：- 解释了广泛用于评估土地覆盖分类准确性的 kappa 统计量，不适用于准确性评估。交替提出了基于Kullback-Leibler散度的判据。 （Nishii 和 Tanaka，1999）- 将森林和人口之间的回归模型扩展到以地形作为新协变量的空间非线性回归。新模型与广岛的实际数据非常吻合。(Tanaka and Nishui, 1997) - 通过空间非线性回归表明地方政府的肺癌死亡率与距海岸线的距离显着相关。这表明需要进一步的流行病学研究。 （准备中）- 为了去噪目的，通过小波函数扩展时间序列数据，并使用贝叶斯模型缩小小波系数。该过程被证明可以成功地对各种数值示例进行去噪。 （准备中）

项目成果

R.Nishii and S.Tanaka: "Accuracy and inaccuracy assessments in land-cover classification" IEEE Transactions on Geo-science and Remote Sensing. 37(1). 491-498 (1999)

R.Nishii 和 S.Tanaka：“土地覆盖分类的准确性和不准确性评估”IEEE 地球科学和遥感学报。

S.Tanaka and R.Nishii: "A model of deforestation by human population interactions" Environmental and Ecological Statistics. 4. 83-91 (1997)

S.Tanaka 和 R.Nishii：“人类相互作用造成的森林砍伐模型”环境和生态统计。

S.Tanaka and R.Nishii: "A model of deforestation by human population interactions" Enuironmental and Ecological Statistics. 4. 83-91 (1997)

S.Tanaka 和 R.Nishii：“人类相互作用造成的森林砍伐模型”环境与生态统计。

M.Yoshikawa, S.Fujimura, S.Tanaka and R.Nishii: "Significance-weighted classification by triplet tree" Proceedings of 1997 IEEE International Geoscience and Remote Sensing Symposium. 658-660 (1997)

M.Yoshikawa、S.Fujimura、S.Tanaka 和 R.Nishii：“通过三重树进行显着性加权分类”1997 年 IEEE 国际地球科学和遥感研讨会论文集。

R.Nishii,S.Kusanobu and N.Nakaoka: "Selection of variables and neighborhoods for Spatial enhancement of thermal infrared images" to appear in communication in Statistics Theory and Methodology. (1999)

R.Nishii、S.Kusanobu 和 N.Nakaoka：“热红外图像空间增强的变量和邻域的选择”出现在《统计理论与方法论》通讯中。