Forest canopy closure from classification and spectral unmixing ofscene components-multisensor evaluation of an open canopy

Three types of remote sensing data, color infrared aerial photography (CIR), compact airborne spectrographic imager (CASI) imagery, and airborne visible/infrared imaging spectrometer (AVIRIS) imagery, have been used to estimate forest canopy closure for an open-canopy forest environment. The high-spatial-resolution CIR and CASI images were classified to generate forest canopy closure estimates. These estimates were used to validate the forest canopy closure estimation accuracy obtained using the AVIRIS image. Reflectance spectra extracted from the spectral-mode CASI image were used to normalize the raw AVIRIS image to a reflectance image. Classification and spectral unmixing methods have been applied to the AVIRIS image. Results indicate that both the classification and the spectral unmixing methods can produce reasonably accurate estimates of forest canopy closure (within 3 percent agreement) when related statistics are extracted from the AVIRIS image and relatively pure reflectance spectra are extracted from the CASI image. However, it is more challenging to use the spectral unmixing technique to derive subpixel-scale components whose reflectance spectra cannot be directly extracted from the AVIRIS image     

Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain)

Saline wetlands in the semiarid environment of Central Spain are fragile and highly dynamic ecosystems that are affected by degradation processes as a result of anthropological influences. An increase in agricultural production has led to the development of large-scale irrigation schemes with overexploitation of groundwater, and with consequent effects on the complex hydrology and associated land use. In this work, data from field, hyperspectral airborne, and multispectral satellite sensors are used in order to determine changes of wetland characteristics over time. The spectra of surface components (soil, vegetation, and salt crusts) were selected from the hyperspectral data and identified as endmembers using a site-specific spectral library. The spectral information contained in these endmembers was extrapolated to a temporal series of broadband multispectral imagery on which spectral unmixing analysis was performed in order to detect changes in the wetland over time. Results showed that the selected wetland components have undergone important changes in both their total area as well as their spatial distribution. These changes are mainly associated with the anthropogenic impact; however, natural influences due to seasonal fluctuations may coincide with the overall changes, although this in general is difficult to determine. Water regulation and agricultural practices directly influence the salinity of the soils and therefore the nature of the hygrophytic vegetation.     

基于空-谱特征K-means的长波红外高光谱图像分类

高光谱图像(hyper spectral imagery,HSI)分类已成为探测技术的重要研究方向之一,同时也在军事和民用领域得到广泛运用。然而,波段数目巨大、数据冗余、空间特征利用率低等因素已成为高光谱图像分类的挑战,且现有的高光谱分类大多利用可见光或短波红外高光谱数据分类。针对这些问题,本文提出了一种基于光谱和空间特征的K-means分类方法。首先提取空间特征,然后将光谱与空间特征相结合并降维,最后引入K-means算法得到较普通K-means更佳的分类结果。并将此算法运用在长波红外的高光谱图像分类中。     

Visible/Infrared/Microwave Agriculture Classification, Biomass, and Plant Height Algorithms

This paper describes the results of a study to determine if visible, infrared, and microwave data is correlated to crop-canopy characteristics (biomass and crop height) and can improve estimates of crop acreage. The objectives were to 1) determine if different crops can be discriminated using multifrequency microwave data, and 2) determine which visible, infrared, and microwave spectral regions can classify crops and correlate well to plant biomass, crop height, and the perpendicular vegetation index (PVI). The study was conducted at Dalhart, Texas, in 1980. Aircraft multispectral data collected during the study included visible and infrared data and active multifrequency microwave data. Ground-truth data from each field consisted of soil moisture, total plant biomass, and crop height. Results indicated that C- (4.75 GHz), L- (1.6 GHz), and P- (0.4 GHz) band active microwave data combined with visible and infrared data maintained or improved crop-discrimination accuracy compared to models using only visible and infrared data. The active microwave frequencies were more sensitive to plant height differences than total biomass differences; the K- (13.3 GHz) and C-bands were sensitive to height variations in short plants, while the P-band was sensitive to differences between tall and short plants.     

利用图谱特征解析和反演作物叶绿素密度

地面成像光谱仪可对作物个体及群体信息进行图谱同步解析,因此在农业定量化研究中具有巨大的应用潜力。利用可见-近红外成像光谱仪采集不同生育期玉米和大豆的冠层图谱数据,在逐步提取影像中光照土壤、阴影土壤、光照植被、阴影植被四种组分光谱的基础上,通过选取的敏感波段构建光谱植被指数和叶绿素密度进行波段自相关分析,探讨各个分量对作物叶绿素密度反演的影响。研究发现:当植被与土壤混合存在时,对叶绿素密度敏感的波段基本在红光与近红外波段;当植被光谱提纯后（剔除土壤光谱）,对叶绿素密度敏感的波段范围增大,表现在蓝、绿波段;当阴影叶片光谱剔除后,对叶绿素密度敏感的波段表现为可见光波段增加,近红外波段减少,红边波段决定系数最高。上述变化特征在不同作物中有相同的趋势,为探索地面成像光谱仪图谱协同反演作物生化参数进行了有意义的探索。     

The impact of viewing geometry on material discriminability inhyperspectral images

An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. The authors analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. The authors show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. They illustrate the performance of material identification over different viewing angles using simulated forest and desert hyperspectral digital imagery collection experiment (HYDICE) images     

基于NSST的红外与可见光图像融合算法

针对红外与可见光图像具有不同的特点,提出一种新的基于非下采样剪切波变换（NSST）的红外与可见光图像融合算法.算法首先采用NSST将已配准的红外与可见光图像进行分解,得到低频子带图像和各尺度各方向的高频子带图像;然后对低频子带图像采用一种基于显著图的低频融合规则进行融合,而对高频子带图像的融合,结合人眼视觉特性,采用一种基于改进的区域对比度的融合规则;最后,对融合的低频子带图像和高频子带图像进行NSST逆变换得到融合图像.实验结果表明,该算法能够有效地综合红外与可见光图像中的重要信息,融合效果要优于一般的基于NSCT、NSST的图像融合方法.     

Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every endmember can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping.     

Spatial/spectral endmember extraction by multidimensional morphological operations

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.     

Optical Determination of Short-Wave Modulation by Long Ocean Gravity Waves

Time series short-wave spectra were produced from photographs acquired during the Joint North Sea Waves Project (JONSWAP). The feasibility of using this surface imagery to quantify the nature of short-wave modulation by long waves is demonstrated. Photographically obtained time series spectra are correlated to the long wave slope. The modulation transfer function of short-wave spectral energy versus long wave slope is measured to have a 9(slope)-1 magnitude with a 90° phase at a wind speed of 5 m/s in the short-wave spectral regime between 3 and 30 cm in wavelength. Comparison of these results with measurements by coherent radars and laser slope gauges indicates good agreement in the measured modulation magnitude but significant disagreement in measured phase. Modulation magnitude and phase predicted from the action balance model are shown to be significantly smaller than empiricaly measured results.     

Validation and refinement of hyperspectral/multispectral atmospheric compensation using shadowband radiometers

First-principles atmospheric compensation of Earth-viewing spectral imagery requires atmospheric property information derived from the image itself or measured independently. A field experiment was conducted in May, 2003 at Davis, CA to investigate the consistency of atmospheric properties and surface reflectances derived from simultaneous ground-, aircraft- and satellite-based spectral measurements. The experiment involved the simultaneous collection of HyMap hyperspectral and Landsat-7 multispectral imagery, in situ reflectance spectra of calibration surfaces, and sun and sky radiances from ultraviolet and visible multifilter rotating shadowband radiometers (MFRSRs). The data were analyzed using several different radiation transport and atmospheric compensation algorithms. Reasonable self-consistency was found between aerosol property retrievals from the radiometers and from dark pixels of the imagery, and, when using the most accurate algorithm, there was excellent agreement between the retrieved surface spectra and the ground truth measurements.     

弱环境辐射下中短波双波段红外图像融合方法

本文介绍了一种用于弱环境辐射下红外中、短波双波段的红外图像融合方法。该方法先采用单尺度Retinex(SingleScaleRetinex,SSR)方法处理短波图像,分别对处理后的短波图像和中波图像采用均值滤波和双边滤波得到细节层和背景层,再利用基于SpectralResidual(SR)显著性和引导滤波的权重映射,对短波图像和中波图像的细节层和背景层进行加权融合,得到融合图像。经过实验仿真,并与基于小波、拉普拉斯金字塔的多种算法做了比较,该方法在各场景下的主观与客观评价均表现良好。     

一种基于EASSF的红外与可见光图像视觉保真度融合

最近,多尺度特征提取被广泛应用于红外与可见光图像融合领域,但是大多数提取过程过于复杂,并且视觉效果不佳。为了提高融合结果的视觉保真度,本文提出一种基于边缘感知平滑锐化滤波器（Edge-Aware Smoothing-Sharpening Filter,EASSF）的多尺度图像融合模型。首先,提出一种基于EASSF的多尺度水平图像分解方法对源图像进行分解,得到水平方向上的多尺度纹理成分和基础成分;其次,采用最大值融合规则（Max-Fusion, MF）融合纹理成分,避免图像细节信息的丢失;然后,通过一种感知融合规则（Perceptual-Fusion, PF）融合基础成分,捕获显著性目标信息;最后,通过整合融合后的多尺度纹理成分和基础成分得到融合图像。实验通过分析感知融合系数,对比融合结果的客观数据得出红外与可见光图像融合在多尺度EASSF下较为合适的取值范围;在该取值范围内,本文提出的融合模型同一些较为经典和流行的融合方法相比,不仅解决了特征信息提取的复杂性,而且通过整合基础成分的显著性光谱信息,有效地保证了融合结果的视觉保真度。     

一种使用灰度梯度提取合成孔径雷达图像灰度边界的方法

在合成孔径雷达数字成像的多级成像和合成孔径雷达图像的许多应用中,往往需要提取合成孔径雷达图像的灰度边界特征。人们对于由可见光或红外成像系统获得的普通图像的灰度边界提取已经进行了很多研究。使用灰度梯度提取普通图像的灰度边界非常有效,得到了广泛的应用.但由于在合成孔径雷达图像中存在严重的相干斑噪声,有人认为使用灰度梯度提取合成孔径雷达图像的灰度边界得不到好的结果。在本文中,我们考虑到合成孔径雷达图像的特性,给出了一种比较理想的提取合成孔径雷达图像的灰度边界的方法。这种方法使用相对平均灰度梯度,并且把检测与跟踪结合起来。     

基于LatLRR和PCNN的红外与可见光融合算法

针对光谱差异较大的红外与可见光图像，本文提出一种基于潜在低秩表示（LatLRR）和脉冲式耦合神经网络（PCNN）的多尺度融合模型。首先，该算法利用非下采样剪切波变换（NSST）获取图像的低频与高频分量。鉴于图像的低频分量决定最终的融合效果，采用LatLRR算法挖掘源图像内在的显著特征对低频分量自适应加权融合。除此外，针对决定融合图像细节的高频分量，则利用双通道PCNN模型作为它的融合规则。其中平均梯度算子（AVG）和方向梯度和算子（SDG）分别作为PCNN的外界刺激与链接强度，它们能更好地表征图像的纹理特性。通过上述全新的融合规则，可将包含在红外图像内部的显著性特征与可见光图像的梯度特征完美结合，从而获取具有优良视觉效果的融合图像。本文采用3种不同的场景来测试所提方法的融合性能，与其他典型融合方法相比，本文提出的算法具有更佳的视觉效果，同时客观评价参数值增加约2%~5%。     

Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards

Traditionally, spectral mixture analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra hampers the implementation of SMA for steering weed control management practices. To address this problem, this paper presents an alternative SMA technique, referred to as Integrated Spectral Unmixing (InSU). InSU combines both magnitude (i.e., reflectance) and shape (i.e., derivative reflectance) related features in an automated waveband selection protocol. Analysis was performed on different simulated mixed pixel spectra sets compiled from in situ-measured weed canopy, Citrus canopy, and soil spectra. Compared to traditional linear SMA, InSU significantly improved weed cover fraction estimations. An average decrease in fraction abundance error (Deltaf) of 0.09 was demonstrated for a signal-to-noise ratio (SNR) of 500 : 1, while for a SNR of 50 : 1, the decrease was 0.06.     

Reducing the dimensionality of plant spectral databases

Ground-based measurements of plant reflectance and transmittance are essential for remote sensing projects oriented toward agriculture, forestry, and ecology. This paper examines the application of principal components analysis (PCA) in the storage and reconstruction of such plant spectral data. A novel piecewise PCA approach (PPCA), which takes into account the biological factors that affect the interaction of solar radiation with plants, is also proposed. These techniques are compared through experiments involving the reconstruction of reflectance and transmittance curves for herbaceous and woody specimens. The spectral data used in these experiments were obtained from the Leaf Optical Properties Experiment (LOPEX) database. The reconstructions were performed aiming at a root-mean-square error lower than 1%. The results of these experiments indicate that PCA can effectively reduce the dimensionality of plant spectral databases from the visible to the infrared regions of the light spectrum, and that the PPCA approach can further maximize the accuracy/cost ratio of the storage and reconstruction of plant spectral reflectance and transmittance data.     

Spectral Variability of LANDSAT-4 Thematic Mapper And Multispectral Scanner Data for Selected Crop And Forest Cover Types

The performance of both the Landsat-4 TM and MSS sensors is evaluated through the analysis of image and digital data simultaneously acquired over agricultural and forestry study sites in California. Spectral statistics extracted for selected cover types include band means, variances, coefficients of variation, range values, skewness, kurtosis, and covariance and correlation matrices. Spectral characteristics are evaluated through analysis of these statistics and interpretation of image products. Image products are used to visually represent significant spectral variations between the TM bands. Significant results include: 1) the overall spectral, spatial, and radiometric quality of the TM data are excellent; 2) discrimination of crop types on single-date image data is significantly improved by the addition of the first short-wave infrared band (TM5); 3) the thermal infrared data (TM6) allows the discrimination of agricultural and forestry cover types based on differences in their radiant temperature responses; and 4) the higher TM spatial resolution (28.5 m versus 57 m) provides the ability to discriminate small agricultural fields and boundaries, forest stand boundary conditions, road and stream networks in rough terrain, and small clearings resulting from various forest management practices.     

星用短波红外成像仪的设计与实现

针对低轨卫星激光通信和大气层短波红外光谱分析的应用需求,设计并研制了一种星用短波红外成像仪。由于低轨卫星系统的抗总剂量辐射能力要求为20kRad(Si)以上,因此,该成像仪选用抗总剂量辐射能力达到20kRad(Si)的InGaAs焦平面探测器;采用宇航级元器件设计相应的时序驱动、温度控制、模数转换、图像传输、遥控遥测等硬件电路模块,组成成像仪硬件部分;应用PID温控算法实现成像仪精准控温,采用非均匀性校正算法和图像增强算法提升成像仪图像质量。经试验验证,研制的短波红外成像仪抗总剂量辐射能力达到25kRad(Si),动态范围大于等于61dB,非均匀性小于等于1.9%,满足低轨卫星短波红外光谱探测的要求。     

基于多模态的车辆行人检测技术研究

针对单一传感器在复杂路况以及恶劣天气情况下车辆行人检测效果不佳,搭建了一套可见光、可见光偏振、短波红外和长波红外多模态数据采集系统,构建了一个多模态数据集,并提出了一种多模态车辆行人检测算法。首先,提出了一种基于改进型SIFT特征点的多尺度部分强度不变特征的异源图像配准算法;然后,提出基于YOLOv5多模态数据目标检测网络。最终实现了平均精度在日间数据集1.0%的提升,日间夜间混合数据集10.9%的提升。