航空红外光电遥感技术最新进展

航空红外光电遥感技术具有可全天时工作、机动灵活、空间分辨率高等不可替代的优势,是遥感科学、国土监测、国防应用等领域的重要手段.发展航空红外光电遥感技术对我国的经济发展和国防建设至关重要.近年来,航空红外光电遥感技术发展很快,在高光谱分辨率红外成像和高空间分辨率红外成像方面取得了重大突破.高空间分辨率、高光谱分辨率、高时...     

Automatic Georeferencing of Airborne Pushbroom Scanner Images With Missing Ancillary Data Using Mutual Information

We describe a methodology that is used for the automatic georeferencing of multispectral images acquired from airborne pushbroom imaging cameras. This methodology is based upon the dense registration of flight strips data onto a reference orthoimage and uses the mutual information criterion to make the raw (source) image and the orthoimage (target) aligned. By taking into account a rigorous modeling of the pushbroom imaging process, we show how the mutual information between the raw image and the orthoimage can be used to estimate unknown or inaccurate flight attitude parameters [(tilt bias angles, instantaneous yaw, and height above ground level (AGL)] without using any ground control nor tie points. Moreover, we show how a coarse digital elevation model can be incorporated in the proposed procedure to improve the geocorrection and even estimate a digital surface model of the surveyed area. Two experiments using Compact Airborne Spectrographic Imager (CASI) and Airborne Imaging Spectroradiometer for Applications (AISA) Eagle sensor data are described and show the effectiveness of our approach, with planimetric root mean square errors being in the range of two to four pixels, depending on the flatness and land cover, and altimetric root mean square errors being less than 1% of the flight altitude AGL.     

The Frequent Image Frames Enhanced Digital Orthorectified Mapping (FIFEDOM) Camera for Acquiring Multiangular Reflectance From the Land Surface

The Frequent Image Frames Enhanced Digital Orthorectified Mapping (FIFEDOM) camera was designed to provide a cost-effective remote-sensing method for accurate acquisition of forest information, such as spatial distributions of individual tree species and tree structures for forest monitoring and management. Compared with existing regular digital cameras, the FIFEDOM camera has several unique features as follows: (1) it can collect data not only in the visible bands (550 and 670 nm) but also in the near-infrared band (800 nm); (2) it has a frame rate of up to 3 frames/s with a frame size of 3500 times 2300; and (3) it has a wide angular field view with 150deg along track and 78.8deg across track. Its high frame rate and wide angular field view allow it to obtain a sequence of images that oversample ground target areas. The multiangle database and bidirectional reflectance signatures of forest canopies can be generated from the oversampled image data, which can be used to identify forest species and estimate tree structures. In addition, the multiframe highly overlapped FIFEDOM data can also be used to generate a very dense, high-quality, and reliable digital surface model. Effective methods for radiometric and geometric calibration of the FIFEDOM camera were developed in this paper. A data-acquisition campaign was carried out in 2004 over the Algoma boreal forest, Ontario, Canada. The FIFEDOM data were validated using the data acquired by the Compact Airborne Spectrographic Imager instrument, which was flown together with the FIFEDOM camera.     

A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method.     

Design and preflight performance of ASTER instrument protoflightmodel

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an advanced multispectral imager with high spatial, spectral, and radiometric resolution, built to fly on the EOS-AM1 spacecraft along with four other instruments, which will be launched in 1998. The ASTER instrument covers a wide spectral region, from visible to thermal infrared with 14 spectral bands. To meet the wide spectral coverage, optical sensing units of ASTER are separated into three subsystems: visible and near-infrared (VNIR) subsystem, shortwave infrared (SWIR) subsystem, and thermal infrared (TIR) subsystem. ASTER also has an along-track stereoscopic viewing capability using one of the near-infrared bands. To acquire the stereo data, the VNIR subsystem has two telescopes, one for nadir and another for backward viewing. Several new technologies are adopted as design challenges to realize high performance. Excellent observational performances are obtained by a pushbroom VNIR radiometer with a high spatial resolution of 15 m, a pushbroom SWIR radiometer with high spectral resolution, and a whiskbroom-type TIR radiometer with high spatial, spectral, and radiometric resolutions. The preflight performance is evaluated through a protoflight model (PFM)     

Generalized IHS‐Based Satellite Imagery Fusion Using Spectral Response Functions

Image fusion is a technical method to integrate the spatial details of the high‐resolution panchromatic (HRP) image and the spectral information of low‐resolution multispectral (LRM) images to produce high‐resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high‐quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity‐hue‐saturation and wavelet‐based methods.     

Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method.     

基于CNN的QuickBird遥感影像融合研究

为了充分利用QuickBird多光谱影像的光谱信息和全色影像的高空间分辨率信息,提出了一种基于细胞神经网络（CNN）的QuickBird多光谱和全色遥感影像融合方法.实验结果表明,相对于传统的影像融合算法,提出的方法具有更好的性能.     

ASTER Level-1 data processing algorithm

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an advanced multispectral imager with high spatial, spectral, and radiometric performance built for the EOS-AM1 polar orbiting spacecraft. ASTER covers a wide spectral region from visible to thermal infrared with 14 spectral bands. To meet this wide spectral coverage, ASTER has three optical-sensing subsystems: visible and near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR). In addition, the VNIR subsystem has two telescopes (nadir and backward telescopes) for stereo data acquisition. This ASTER instrument configuration with multitelescopes requires highly refined ground processing for the generation of Level-1 data products that are radiometrically calibrated and geometrically corrected. The algorithm developed for the ASTER Level-1 data processing is described     

一种机载红外光谱相机的角位置误差补偿技术

机载红外多光谱扫描仪采用摆扫扫描成像机制,解决了红外光谱相机的光谱分辨率、高空间分辨率和大成像幅宽之间的矛盾,可实现下视和远距离侧视成像。为了实现采集图像无缝拼接的像元级对准,需保证红外光谱仪的角位置信息准确。本文针对机载扫描成像光谱仪的几何定位问题进行分析,指出角位置误差是影响相机几何定位的主要因素,进而采用一种角位置误差的长短周期双重补偿方法,对角位置误差进行补偿。地面测试结果表明,补偿后相机角位置精度提高10倍,且经环境试验验证,角位置误差仍保持稳定。由机载挂飞试验结果表明,后图像相对几何精度优于一个像元（10）,满足图像拼接的几何定位需求。     

Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data

This paper presents a method of unsupervised enhancement of pixels homogeneity in a local neighborhood. This mechanism will enable an unsupervised contextual classification of multispectral data that integrates the spectral and spatial information producing results that are more meaningful to the human analyst. This unsupervised classifier is an unsupervised development of the well-known supervised extraction and classification for homogenous objects (ECHO) classifier. One of its main characteristics is that it simplifies the retrieval process of spatial structures. This development is specially relevant for the new generation of airborne and spaceborne sensors with high spatial resolution.     

基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法

多光谱遥感图像的压缩要利用图像谱内及谱间的相关特性。该文在分析多光谱图像谱内和谱间相关特性的基础上,提出了对多光谱遥感图像进行压缩的分段 DPCM和 SPIHT相结合的混合压缩算法,即首先利用分段 DPCM算法去除谱间冗余,再利用高效的 SPIHT小波压缩算法对预测误差图像进行编码。实验取得了令人满意的效果,证明了该算法的有效性。     

联合多流融合和多尺度学习的卷积神经网络遥感图像融合方法

为尽可能保持原始低分辨率多光谱(LRMS)图像光谱信息的同时,显著提高融合后的多光谱图像的空间分辨率,该文提出一种联合多流融合和多尺度学习的卷积神经网络遥感图融合方法.首先将原始MS图像输入频谱特征提取子网得到其光谱特征,然后分别将通过梯度算子处理全色图像得到的梯度信息和通过卷积后的全色图像与得到的光谱特征图在通道上拼...     

联合NDRI特征和空间相关性的机载MS-LiDAR数据分类

对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明：算法的平均总体精度和Kappa系数分别可达90.17%和...     

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

The volume of data grows with the advent of multiple types of remote sensing sensors and in order to extract the most useful information there is a need to combine the data gathered from the different sources. The widely used panchromatic and multispectral imageries in many applications offer decimetric and metric spatial resolution. However, the spectral resolution of these images is poor. Hyperspectral imaging has unique characteristics of providing very fine spectral resolution in a large number of bands with decametric spatial resolution and found to be highly useful for a wide span of application areas that requires high spectral resolution. The fusion of spectral and spatial information provides an effective way of enhancing the spatial quality of hyperspectral imagery as well as a method for preserving spectral quality. This fusion process is not a trivial task as always there has been a tradeoff between the preservation of spatial and spectral quality information as in the original sources of fusion. In this paper, a review on hyperspectral pansharpening and hyperspectral multispectral fusion based approaches has been reported. The widely adopted quantitative and qualitative performance measures to verify the fusion results are highlighted. In addition, the challenges in existing fusion techniques have also been discussed.     

基于统计模型的遥感图像多分辨融合方法

本文针对多光谱图像的增强问题,提出了一种新的多分辨融合方法.将相关约束引入到统计融合模型中,从而充分增强了相关的空间信息,同时又有效抑制了光谱失真.真实的遥感图像实验结果表明了该文算法的有效性.     

Texture feature analysis using a gauss-Markov model in hyperspectral image classification

Texture analysis has been widely investigated in the monospectral and multispectral imagery domains. At the same time, new image sensors with a large number of bands (more than ten) have been designed. They are able to provide images with both fine spectral and spatial sampling, and are called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, using a Gauss-Markov random field (MRF). The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas. These areas are not well characterized by radiometry alone, and so we use the MRF parameters as new features in a maximum-likelihood classification algorithm. The results obtained on Airborne Visible/Infrared Imaging Spectrometer hyperspectral images demonstrate that a better classification is achieved when texture information is included in the analysis.     

基于高程和地物光谱约束的多光谱图像预处理算法

分视场滤光片型多光谱相机在利用拼接和配准生成多光谱图像时,受云、水等特殊地物的影响,利用现有方法生成的结果图像上容易出现条带噪声和错配现象。提出了一种三维信息约束下的尺度不变特征转换(SIFT)图像配准和地物光谱特性约束下的多光谱图像预处理算法,该算法利用三维信息约束的SIFT算子提高配准精度,同时在地物光谱特性约束下选取有效的辐射校正点提高图像拼接时各条带图像灰度的校正精度。实验结果表明,利用该方法对分视场多光谱相机数据进行预处理时,即使图像上存在云、水等特殊区域的地物,结果图像仍能保持高精度配准且无条带噪声。     

Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

Hyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by combining the hyperspectral image with a high spatial resolution panchromatic image. These techniques are generally known as pansharpening and can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multispectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Thus, one of the main challenges in hyperspectral pansharpening is to improve the spatial resolution while preserving as much as possible of the original spectral information. An effective solution to these problems has been found in the use of hybrid approaches, combining the better spatial information of CS and the more accurate spectral information of MRA techniques. In general, in a hybrid approach a CS technique is used to project the original data into a low dimensionality space. Thus, the PAN image is fused with one or more features by means of MRA approach. Finally the inverse projection is used to obtain the enhanced image in the original data space. These methods, permit to effectively enhance the spatial resolution of the hyperspectral image without relevant spectral distortions and on the same time to reduce the computational load of the entire process. In particular, in this paper we focus our attention on the use of Nonlinear Principal Component Analysis (NLPCA) for the projection of the image into a low dimensionality feature space. However, if on one hand the NLPCA has been proved to better represent the intrinsic information of hyperspectral images in the feature space, on the other hand an analysis of the impact of different fusion techniques applied to the nonlinear principal components in order to define the optimal framework for the hybrid pansharpening has not been carried out yet. More in particular, in this paper we analyze the overall impact of several widely used MRA pansharpening algorithms applied in the nonlinear feature space. The results obtained on both synthetic and real data demonstrate that an accurate selection of the pansharpening method can lead to an effective improvement of the enhanced hyperspectral image in terms of spectral quality and spatial consistency, as well as a strong reduction in the computational time.