一种新的全色与多光谱图像融合变分模型

图像融合是提供包含各输入图像互补信息的单幅图像的有力工具. 本文提出了一种新的用于全色和多光谱图像融合的变分模型. 在Socolinsky对比度模型的基础上构造了一个改进的能量泛函最小化问题, 以寻找最接近全色图像梯度的解.为了提高多光谱图像的空间分辨率,并尽可能地保持其原有的光谱信息, 还将光谱一致项、波段间相关项和对比度增强项引入融合模型. 在IKONOS和QuickBird数据集上测试了该模型的性能.实验结果表明该模型可以生成同时具有高空间质量和高光谱质量的融合图像.     

Brovey 融合与小波融合对QuickBird 图像的信息量影响

图像融合是解决多源遥感图像综合的最有效技术手段, 针对不同数据源选择最佳的融合方法是提高图像融合质量的关键。在分析了Brovey 融合和小波融合的理论、算法和融合过程的基础上,对QuickBird 的全色波段图像和多光谱波段图像数据进行融合实验, 然后从定性和定量两个方面对融合效果进行了分析与评价。定性分析是从色调、纹理和清晰度等方面进行分析, 而定量分析是根据熵、平均梯度和光谱真实性等指标进行分析, 实验结果表明: 在处理QuickBird 遥感图像时, 采用小波融合的图像既保持了较高的空间分辨率, 又具有较好的光谱特性; 而采用Brovey 融合的图像虽然图像空间分辨率也较高, 但光谱信息丢失较大, 因此Brov ey 融合方法并不适用于处理QuickBird 遥感图像。     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km² of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km² of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha^− 1 (old-growth coast redwood) and 510 ± 120 Mg ha^− 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha^− 1 in Garcia-Mailliard and 140 ± 0.9 Mg ha^− 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management

In the Galapagos Islands of Ecuador, one of the greatest threats to the terrestrial ecosystem is the increasing number and areal extent of invasive species. Increased human presence on the islands has hastened the introduction of plant and animal species that threaten the native and endemic flora and fauna. Considerable research on invasive species in the Galapagos Islands has been conducted by the Charles Darwin Foundation. We complement that work through a spatially- and spectrally-explicit satellite assessment of an important invasive plant species (Psidium guajava — guava) on Isabela Island that integrates diverse remote sensing systems, data types, spatial and spectral resolutions, and analytical and image processing approaches. QuickBird and Hyperion satellite data are processed to characterize the areal extent and spatial structure of guava through the following approaches: (1) QuickBird data are classified through a traditional pixel-based approach (i.e., an unsupervised classification approach using the ISODATA algorithm), as well as an Object-Based Image Analysis (OBIA) approach; (2) multiple approaches for spectral “unmixing” of the Hyperion hyper-spectral data are assessed to construct spectral end-members from QuickBird data using linear and non-linear mixture modeling approaches; and (3) landscape pattern metrics are calculated and compared for the pixel-based, object-based, and spectral unmixing approaches. The spectral–spatial characteristics of guava are interpreted relative to management strategies for the control of guava and the restoration of natural ecosystems in the Galapagos National Park.     

A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery

We have developed and tested a method for mapping above-ground forest biomass of black spruce (Picea mariana (Mill.) B.S.P.) stands in northern boreal forests of eastern Canada. The method uses QuickBird images and applies image processing algorithms to extract tree shadow fraction (SF) as a predictive variable for estimating biomass. Three QuickBird images acquired over three test sites and 108 ground sample plots (GSP) were used to develop and test the method. SF was calculated from the fraction of tree shadow area over the area of a reference square overlaid on the images. Linear regressions between biomass of GSP and SF from the images for each test site resulted in R² in the range from 0.85 to 0.87 (except one case at 0.41), RMSE of 11 to 18 t/ha and bias of 2 to 5 t/ha. Statistical tests demonstrated that local regressions for the three test sites were not statistically significantly different. Consequently, a global regression was calculated with all GSP and produced R², RMSE, and bias of 0.84, 14.2 t/ha and 4.2 t/ha, respectively. While generalization of these results to extended areas of the boreal forest would require further assessment, the SF method provided an efficient means for mapping biomass of black spruce stands for three test areas that are characteristic of the northern boreal forest of eastern Canada (boreal and taiga shield ecozones).     

Image-based atmospheric correction of QuickBird imagery of Minnesota cropland

High spatial resolution QuickBird satellite data have provided new opportunities for remote sensing applications in agriculture. In this study, image-based algorithms for atmospheric correction were evaluated on QuickBird imagery for retrieving surface reflectance (ρ_λ) of corn and potato canopies in Minnesota. The algorithms included the dark object subtraction technique (DOS), the cosine approximation model (COST), and the apparent reflectance model (AR). The comparison with ground-based measurements of canopy reflectance during a 3-year field campaign indicated that the AR model generally overestimated ρ_λ in the visible bands, but underestimated ρ_λ in the near infrared (NIR) band. The DOS-COST model was most effective for the visible bands and produced ρ_λ with the root mean square errors (RMSE) of less than 0.01. However, retrieved ρ_λ in the NIR band were more than 20% (mean relative difference or MRD) lower than ground measurements and the RMSE was as high as 0.16. The evaluation of the COST model showed that atmospheric transmittance (T_λ^θ) was substantially overestimated on humid days, particularly for the NIR band because of the undercorrection of water vapor absorption. Alternatively, a contour map was developed to interpolate appropriate T_λ^θ for the NIR band for clear days under average atmospheric aerosol conditions and as a function of precipitable water content and solar zenith angle or satellite view angle. With the interpolated T_λ^θ, the accuracy of NIR band ρ_λ was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of ρ_λ was acceptable for agricultural applications.     

QuickBird卫星影像处理技术实践

采用卫星遥感方法制作的数字正射影像是获取基础空间信息最快速、高效的手段,利用高精度卫星遥感成图,处理速度快、工艺简便、图像清晰,而且精度达到成图要求。本文重点介绍以QuickBird0.6米全色和2.4米多光谱为数据源,制作DOM的技术要求、方法、工艺流程和质量控制指标。     

QuickBird影像融合算法的评价研究

目前多种成熟的融合算法已经应用在各种遥感软件中,但是融合方法的选择往往会因融合对象的不同而有所差异.为了评价出各个融合算法在QuickBird影像融合上的优缺点,本文在像素级的融合层次土运用多尺度分析的方法进行了融合实验.实验中,根据不同的算法原理引入了七种常用的融合算法,并以空间细节信息、光谱质量以及亮度信息作为统计参数,对实验数据进行了比较研究,分析出了几种融合方法的差异.研究表明,一些传统的融合方法如PCA变换和IHS变换已不适用于QuickBird这种高分辨率影像的融合,而基于小波的PCA变换、小波变换以及HPF变换在实验中有较好的表现.本次实验也为其它高分辨率卫星遥感影像的融合工作提供了参考.