基于自适应注入模型的遥感图像融合方法

遥感图像融合的目的是融合高光谱分辨率、低空间分辨率的多光谱（MS）图像和高空间分辨率、低光谱分辨率的全色（PAN）图像,得到高光谱分辨率与高空间分辨率的融合图像。遥感图像的注入模型中如何确定注入细节及注入系数是该技术研究的关键。针对注入细节优化,先通过模拟MS传感器的特性来定义一种多尺度高斯滤波器,再用该滤波器卷积PAN图像以提取细节,得到与MS图像高度相关的细节。针对注入系数优化,综合考虑光谱信息与细节信息提出一种自适应的注入量系数。为更好地保留边缘信息,提出一种新的边缘保持权重矩阵,实现光谱信息与空间的双保真。将优化后的注入系数与注入细节相乘注入到上采样后的MS图像中,得到融合结果。对所提方法进行性能分析,并在各卫星数据集上进行大量测试,与一些先进的遥感图像融合方法进行对比,实验结果表明,所提方法在主观与综合客观指标上都能达到最优。      

基于框架域的随机游走全色锐化方法

针对多光谱图像与全色图像的融合问题,提出了一种新的全色锐化方法。该方法首先通过亮度、色调、饱和度(IHS)变换与非下采样框架变换将原图像从空间域变换到框架域,然后利用基于图论的随机游走,建立高频框架系数的统计融合模型。此模型根据高频框架系数的邻域相关性与尺度相关性构造新的随机游走协调函数,将高频框架系数融合权重的估计转化为随机游走标记问题的求解。实验结果表明,该方法有利于保持图像的光谱信息和边缘轮廓信息,可以在降低融合图像光谱误差的同时提高空间分辨率,并且优于一些主流全色锐化方法。     

Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process.     

Detailed choice of the spectral bands

The SPOT satellite is designed so as to provide multispectral observations with 20 m resolution and panchromatic observation with 10 m resolution. The three spectral bands allow a study of the spectral signature of objects; so, the detailed profile of these bands has been optimized so as to select the parts of major interest in the reflectance of various objects and minimize the atmospheric perturbation in the quantitative measurement. The panchromatic band is mainly involved in cartographic application; so the design of the spectral sensitivity is related to the study of the contrast between objects thematically characterized, taking into account the actual resolution.     

A reflectance spectra-based approach to mapping salt fields using PCA-fused Landsat TM data

In this paper a method based on in situ measured reflectance spectra was developed for accurately mapping salt fields in the Taibei Salt Farm in Lianyungang City of East China. After radiometric correction and geometric rectification, six multispectral ETM+ bands were fused with the panchromatic band via principal components analysis (PCA). The fused data were used to map salt fields in the farm. Salt fields were mapped 91.95% correctly in comparison with ground statistics from raw multispectral bands. This accuracy level rose to 96.4% with the use of the panchromatic band (15 m resolution). However, PCA-fused data produced the highest accuracy of 98.8%. At the zone level, coarse resolution data resulted in an accuracy of 98.93% for crystallization ponds, but only 82.68% for condensation ponds. Use of the panchromatic band alone improved the accuracy for condensation ponds but decreased the accuracy for crystallization and evaporation ponds. Results derived from PCA-fused data are highly consistent with the ground statistics at a discrepancy between 0.35% for evaporation zone and 1.80% for condensation zone. It is concluded that a fine spectral resolution is important to separation of ponds of a varying salt content while a fine spatial resolution is important to their mapped area. Ponds of a lower salt content can be mapped more accurately from fused data than from raw spectral bands.     

面向高分辨率遥感影像的地形辐射校正方法

地形辐射校正对获取准确的地表定量遥感精度意义重大。针对传统地形辐射校正模型不适用于高分辨率遥感影像的问题,提出了一种基于辐射传输模型,同时严控误差源的地形辐射校正方法,以资源三号01星高分辨率全色及多光谱遥感影像为例进行相关实验,实现对高分辨率遥感影像的地形辐射校正,并进行了主客观分析与评价。分析结果表明:本文提出的地形辐射校正模型和方法,能有效解决全色遥感影像在绝对辐射定标系数缺失情况下校正效果差以及如何保持高分辨率遥感影像细节等难点,较传统方法更适用于高分辨率遥感影像。      

基于卫星遥感的岛礁影像多类目标智能化提取

卫星遥感影像具有背景复杂、目标尺度不一、观测方向各异、纹理不清晰等特点，主流的深度学习目标检测算法不能直接适用于卫星遥感影像的目标检测。改进了RetinaNet，使其适用于卫星遥感影像。首先设计了一种新的特征融合方式，融合ResNet50输出的特征图，使得融合后的特征图同时具有高层语义信息和低层纹理细节信息。为了减弱遥感影像复杂背景对目标特征的影响，设计了特征感知模块，在减弱噪声对特征图影响的同时增强有用特征。挑选DOTA数据集中船只、飞机和存储罐图像进行训练和测试。改进的算法与RetinaNet相比，飞机、船只和存储罐的平均精度分别提高了41%、25%、24%。基于高分二号卫星（GF 2）真实影像数据的试验结果表明，提出的算法能够用于卫星遥感岛礁影像的多类目标智能化提取。     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

Multiple sensor geocoded data

The problem of integrating remotely sensed images acquired from different sensor systems is primarily associated with their registration into a common reference projection. This registration problem has to be considered not only between the image data themselves but also with other data already existing in various geographical data bases.The advent of the second generation of earth observation satellites is emphasizing this situation by providing imagery in different geometric configurations. Images will be acquired from many sensors at different spatial resolutions (LANDSAT MSS and TM, SPOT HRV MLA and PLA) on different dates and modes (panchromatic, multispectral, nadir and off-nadir).In order to provide image data in a geometric format compatible with the Canadian National Topographic System, Canada is developing a Multi-Observational Satellite Image Correction System (MOSAICS) that will provide geocoded data resampled into a common reference projection. This paper presents the characteristics of the geocoded products with examples from multiple sensors and different dates, and addresses the processing requirements of the satellite ground station. It also illustrates the use of digital processing techniques to derive digital elevation information from geocoded stereo imagery.     

基于多种空间信息的高光谱遥感图像分类方法

在高光谱遥感图像分类方法中,空间特征和光谱特征的融合可以有效地改善分类效果。针对单一空间特征的信息表达不充分问题,提出了一种联合多种空间特征的高光谱图像空谱分类方法。利用超像素信息对分类结果进行后处理去掉椒盐噪声,并创造性地将超像素信息应用于分类前处理,提出了一种利用超像素信息对像素点的特征向量进行线性加权融合的方法。试验结果表明,所提方法的性能优于目前的通常方法。     

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from “normal” (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.     

基于非下采样轮廓波变换的红外和SAR图像融合算法

针对现有红外和合成孔径雷达(Synthetic Aperture Radar,SAR)图像的融合算法融合质量差、边缘轮廓不清晰、效率低下、可视性差,目标检测效率低等问题,提出一种基于非下采样轮廓波变换的融合算法。首先采用非下采样轮廓波变换对预处理的红外和SAR图像进行分解,获得各自低频和带通方向图像,接着根据红外和SAR图像的特征选取其含重要目标信息的频带进行低频图像和带通方向图像融合。为了检验本文所提出算法性能的优越性,采用两组红外和SAR图像进行融合实验,与其他图像融合算法进行对比,并对融合图像进行目标检测,证明了该融合算法能有效提高多源图像目标检测率。     

MEMORIS: a wide angle camera for the BepiColombo mission

MEMORIS (MErcury Moderate Resolution Imaging System) is a wide angle camera (WAC) concept for the ESA mission BepiColombo. The main scientific objectives consist of observing the whole surface of Mercury in the spectral range of 400–1000 nm, with a spatial resolution of 50 m per pixel at peri-Herm (400 km) and 190 m at apo-Herm (1500 km). It will obtain a map of Mercury in stereo mode allowing the determination of a digital elevation model with a panchromatic filter through two different channels. The camera will also perform multispectral imaging of the surface with a set of 8–12 different broad band filters. A third channel dedicated to limb observations will provide images of the atmosphere. MEMORIS will thus monitor the surface and the atmosphere during the entire mission, providing a unique opportunity to study the relationship between surface regions and the atmosphere, as suggested by ground-based observations and theory.     

基于生成对抗网络的空间卫星低照度图像增强

针对空间低照度成像条件下卫星光学图像信息受损严重的问题，提出了一种基于生成对抗网络的空间卫星低照度图像增强方法，提高了图像的平均亮度及对比度，恢复图像细节信息，为图像识别等图像处理技术提供更高质量的数据信息。首先，设计了一种密集连接的生成器，加强了各特征提取阶段中的信息传递以及多层特征的融合，减少了特征信息的损耗，更好地提取正常照度图像及低照度图像中相似的语义信息；并结合EnlightenGAN的思想，采用了全局 局部辨别器结构，使图像增强效果更自然。然后，在少量样本的条件下，利用非配对样本对该方法进行训练，并通过对输入图像进行随机缩放及翻转等数据增强方法提高模型训练效果，进而提升低照度图像增强性能。最后，对所提出的空间卫星低照度图像增强方法进行了仿真验证。试验结果表明，在空间低照度条件下，该方法在NIQE指标上较LIME及EnlightenGAN分别降低了1.034和0.699，保留了更多的图像细节，具有更高的整体和局部亮度、更高的对比度以及更自然的增强效果。     

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.     

卫星遥感图像信息作为姿态敏感器的应用研究

陆标敏感器可以从卫星遥感图像信息中提取卫星的姿态信息,它根据卫星实时图像与基准图像之间的偏移量,计算出卫星的姿态。对陆标敏感器的总体方案进行了设计,在图像匹配技术中采用基于区域特征的先粗匹配、后精匹配的匹配算法,在姿态确定算法中对可能达到的姿态测量精度进行了理论分析,研究表明陆标敏感器可以获得较高精度的卫星姿态信息,作为新型光学姿态敏感器具有重要的研究意义。     

多谱段全日面极紫外成像仪

在极紫外波段对太阳进行成像观测是研究太阳活动、日冕中等离子体物理特性的重要手段.传统极紫外成像仪或光谱仪无法同时实现高光谱分辨率和大视场的太阳成像.本文设计了一种新型太阳极紫外多谱段成像系统,采用无狭缝光栅分光方式实现了高光谱分辨率和空间分辨率的全日面成像,成像视场可达47',光谱分辨率每像素2×10-3 nm,空间分辨率每像素1.4',全日面时间分辨率优于60s.通过分析谱线的全日面成像图和系统响应,表明成像仪能大范围的观测太阳活动形态演化,为太阳物理研究和空间天气预报提供更完整的观测数据.      

Space object material identification method of hyperspectral imaging based on Tucker decomposition

Space object material identification method based on Tucker decomposition is proposed and demonstrated experimentally. Space target generally has a low spatial resolution because of the limitation in detection distance. Hyperspectral imaging (HSI) technology can capture the image from visible light to shortwave infrared continuously while providing the spatial and spectral information of the target, thereby introducing a new space target detection and identification approach. However, the data obtained by the HSI system often contain mixed pixels, thereby causing difficulties in target material identification. In this work, a material identification method of hyperspectral data based on Tucker decomposition is proposed by combining mixed spectral theory with tensor decomposition. The feasibility of the method is verified by using satellite model hyperspectral data with different spatial resolutions compared with the endmember obtained from the non-negative matrix decomposition (NMF), independent component analysis (ICA), tensor singular value decomposition (t-SVD). The average CORR of NMF, ICA, t-SVD and the proposed method is 0.2694, 0.5818, 0.6397 and 0.937, correspondingly. Therefore, the proposed method has demonstrated a more remarkable performance in terms of material identification, the analysis results of the material abundance distribution that used the proposed method.