An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

基于PALSAR数据的DEM提取方法研究

针对PALSAR Level 1.1数据,研究使用NASA/JPL提供的开源干涉软件包ROI_PAC Version 3.0提取DEM.ROI_PAC的目前版本只能处理Level 1.0数据,因此,文章在分析了ROI_PAC软件包处理流程的基础上,提出处理Level 1.1数据的方法,并用PALSAR Level 1.1数据对该方法做了验证.干涉重建DEM与参考DEM的对比结果表明,二者的差异均值为0.27 m,标准差为±9.24 m,80%像元点的高程误差在±10 m以内.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

多源数据在甘肃山羊坝地区找矿勘查中的应用

针对单一遥感数据已难以满足地质找矿工作需求的问题,本次研究综合使用雷达数据、光学数据及其他非遥感数据共同服务于地质找矿。以甘肃山羊坝地区为研究区,选择ASTER多光谱遥感数据,采用植被抑制法+特征向量主成分分析法,提取研究区的蚀变信息;选择PALSAR雷达数据,采用聚焦、多视、滤波、辐射定标、地理编码和增强处理等一系列处理方法制作雷达强度图,提取研究区构造信息。最后利用GIS平台进行遥感、地质及化探等信息的集成与综合分析,最终圈定了具有找矿前景的矿产资源靶区,野外查证发现一处金矿点。此次研究获得了良好的找矿效果,表明同时使用雷达数据、光学数据及其他非遥感数据的综合找矿方法,对本地区金矿找矿勘查具有重要的指导作用。     

ALOS PALSAR全极化图像分类研究

极化干涉相干矩阵服从复Wishart分布,通过对相关系数的分析可以获得不同的地物类别。在总结极化干涉非监督Wishart ML分类流程的基础上,基于该方法对塔河地区全极化PALSAR数据进行了分类,研究结果表明:基于极化干涉的分类方法能够有效区分不同散射机制对应的地物,该分类方法具有较强的适应性,并且类间边界比较明显,这些分类信息为森林资源的开发和利用提供了参考。     

基于PCI的ALOS融合正射影像图制作——以果化石漠化监测区为例

为满足特殊岩溶地貌对遥感影像正射纠正的要求,以广西果化石漠化监测区为研究区,利用PCI中的OrthoEngine模块,依据1∶1万数字化等高线制作DEM;以ALOS全色影像为数据源,通过RPC有理函数模型进行正射纠正,采用PANSHARP融合算法,高保真地将全色影像与多光谱影像进行融合,制作成空间分辨率为2.5 m的融合正射影像图。结果表明,此方法为岩溶区的科学研究提供了精度高和信息丰富的现时数据源,为岩溶石漠化监测提供了科学的信息更新手段。     

基于ALOS数据的乌鲁木齐市绿地景观格局研究

以乌鲁木齐市2009年的ALOS卫星影像为研究数据,在地理信息系统(GIS)及遥感(RS)技术的支持下,提取绿地信息,建立乌鲁木齐市区绿地景观的空间数据库,应用景观生态学的理论和研究方法,选取景观分维数、破碎度、分离度等景观指数来定量分析乌鲁木齐市区的绿地景观格局。结果表明,整个研究区内的绿地类型以公园绿地和单位附属绿地为主,市区绿地景观多样性较低,各类型绿地景观所占比例不均匀,公共绿地数量少,趋于团聚分布,形成许多绿化服务盲区;各类型绿地景观分维数较高,形状较不规则;单位附属绿地和居住绿地的破碎度较高,因为它们受人类的影响较大、形状相对多样、不规则。     

Polarimetric scattering model for estimation of above ground biomass of multilayer vegetation using ALOS-PALSAR quad-pol data

Forests are important biomes covering a major part of the vegetation on the Earth, and as such account for seventy percent of the carbon present in living beings. The value of a forest’s above ground biomass (AGB) is considered as an important parameter for the estimation of global carbon content. In the present study, the quad-pol ALOS-PALSAR data was used for the estimation of AGB for the Dudhwa National Park, India. For this purpose, polarimetric decomposition components and an Extended Water Cloud Model (EWCM) were used. The PolSAR data orientation angle shifts were compensated for before the polarimetric decomposition. The scattering components obtained from the polarimetric decomposition were used in the Water Cloud Model (WCM). The WCM was extended for higher order interactions like double bounce scattering. The parameters of the EWCM were retrieved using the field measurements and the decomposition components. Finally, the relationship between the estimated AGB and measured AGB was assessed. The coefficient of determination (R²) and root mean square error (RMSE) were 0.4341 and 119 t/ha respectively.     

浅谈利用遥感卫星ALOS1B1级数据制作外业调绘用图的方法——以芜湖县第二次土地调查为例

全国第二次土地调查于2007年下半年启动,2009年完成,安徽省于2008年4月率先启动了芜湖县的试点工作,并自行购买了ALOS1B1级数据制作DOM,套合上矢量的原始土地利用现状图,以便于外业调绘的全面开展.下面,是笔者对利用ALOS1B1级数据制作正射影像图以及外业调绘用图的方法进行了探讨,研究发现,此方法易于掌握,调绘底图的生产快速,质量可靠,效果显著.     

基于SBAS/PS-InSAR技术的滑坡遥感监测对比研究

以2015~2019年12景ALOS-2 PALSAR2影像和2018~2019年38景Sentinel-1A影像为主要数据源,利用PS-InSAR和SBAS-InSAR技术提取西藏江达县波罗乡白格滑坡点的形变信息,并对处理结果进行交叉验证。研究得到以下结论：1）PS-InSAR技术条件下,ALOS-2数据和Sentinel-1A数据的平均形变速率范围为-68.9~37.9 mm/a和-64.5~24.2 mm/a;SBAS-InSAR技术条件下,ALOS-2数据和Sentinel-1A数据的平均形变速率范围为-84.2~-40.0 mm/a和-84.0~-13.0 mm/a。2）对2种数据结果中提取的4个特征点进行时序分析和定量分析显示,2种InSAR技术结果变化趋势较为一致,验证了两者在滑坡监测中的可靠性和准确性。