Multispectral line-scan imaging system for simultaneous fluorescence and reflectance measurements of apples: multitask apple inspection system

In this methodology paper, a recently developed line-scan imaging system, capable of simultaneously acquiring a combination of multispectral reflectance and fluorescence from rapidly moving objects, is presented. The system can potentially provide multitask inspections for quality and safety attributes of apples due to its dynamic selectivity in multispectral bands, each with independent spectral bandwidth in the fluorescence and reflectance domains. Mounted on a commercial apple-sorting machine, the system was evaluated to determine the image pixel (spatial) resolution for apples at a sorting line speed of three to four apples per second. Apples loaded on the sorting machine were spaced approximately 15 cm apart. With these parameters, the resulting images showed approximately 40 line-scan images per apple, for a horizontal spatial resolution of 2 mm per pixel. In the vertical direction, with 1,002 pixels available for each line-scan image, the spatial resolution of the system can be as high as approximately 0.2 mm per pixel depending on the choice of binning. The combined spatial resolution is comparable to that in our previous studies and is adequate for image-based online inspection of defects and fecal contamination on apples. Company and product names are used for clarity and do not imply any endorsement by USDA to the exclusion of other comparable products.     

植被健康指数及高等级公路路域环境评价研究

植被是环境的重要组成因子,植被的保护和恢复是公路生态环境建设的核心。而生态环境的传统评价方法无法对植被环境做出全面、及时的反映,无法满足实际工作需求。利用遥感的光谱性优势,以京港澳高速长潭段路域为试验区,结合物理方法及数学分析,建立高等级公路路域植被健康指数模型。结果表明:模型计算结果与现场试验结果吻合较好,可有针对性地指导路域环境的防治,为路域生态环境遥感监测的业务化运行奠定坚实基础。     

基于神经网络的多光谱遥感图像无损压缩

分析并改进了利用自组织特征映射(SOFM)神经网络设计码书的方法,提出了一种基于改进SOFM算法设计码书的矢量量化和分类谱间预测相结合的多光谱图像无损压缩方法。该方法对光谱信息进行矢量量化,根据分类信息生成残差图像以去除数据的空间相关性,构造分类谱间预测器去除数据的谱间结构和统计相关性。对机载64波段多光谱遥感图像的试验结果表明,该方法无论是对训练集内图像还是训练集外图像,均取得了较好的压缩效果,平均无损压缩比达到3.2以上。     

小波变换在多光谱图象压缩中的应用

机载高分辨率成象光谱仪所记录的数据量很大,而采用普通的压缩方法其精度满足不了事后处理的要求。因此利用小波变换,将图象分解成不同的频率区域,再根据多光谱图象的特点进行编码压缩,较好地完成了压缩编码的任务,与通用的压缩方法相比,不易产生细节模糊,具有更高的精度。     

基于局部信息熵及其分布特性的图像融合算法

提出了一种新的基于HIS模型的图像融合算法,根据源图像在有向梯度域中的局部信息熵及其分布情况确定融合准则。对实验结果的主观定性评价和客观定量分析说明,新算法在有效保持空间信息的同时,产生的光谱畸变较小,对全色图像中的噪声有一定的免疫力,从而克服了现有同类算法光谱失真严重和对源图像中噪声敏感的缺点。     

一种超高分辨率遥感图像融合新算法

该文针对超高分辨率的全色光图像和多光谱图像的融合,提出了一种基于对应分析的图像融合新算法。该算法在对多光谱数据进行对应分析的基础上,利用冗余小波变换提取出全色光图像的空间细节信息并将其融入到成分空间。实验分别采用IKONOS和QuickBird数据,融合结果的目视效果与客观评价表明,相比现有同类融合方法,该方法能够在提高空间分辨率的同时更好地保持光谱特性,有效地减少了色彩失真的现象。     

一种细胞分割新方法

为了解决传统的细胞彩色图像分割中遇到的诸多问题,采用了一种新颖的多光谱成像技术进行图像采集,并探索性地将支持向量机用于骨髓细胞图像的分割。实验证明这种以多光谱技术为基础的分割方法推广性好,准确率非常高,并且对采集设备状况和涂片质量依赖性低。     

A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification

For real-world simulation, terrain models must combine various types of information on material and texture in terrain reconstruction for the three-dimensional numerical simulation of terrain. However, the construction of such models using the conventional method often involves high costs in both manpower and time. Therefore, this study used a convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify the construction of future models. Visible light (i.e., RGB), near infrared (NIR), normalized difference vegetation index (NDVI), and digital surface model (DSM) images were examined.This paper proposes the use of the robust U-Net (RUNet) model, which integrates multiple CNN architectures, for material classification. This model, which is based on an improved U-Net architecture combined with the shortcut connections in the ResNet model, preserves the features of shallow network extraction. The architecture is divided into an encoding layer and a decoding layer. The encoding layer comprises 10 convolutional layers and 4 pooling layers. The decoding layer contains four upsampling layers, eight convolutional layers, and one classification convolutional layer. The material classification process in this study involved the training and testing of the RUNet model. Because of the large size of remote sensing images, the training process randomly cuts subimages of the same size from the training set and then inputs them into the RUNet model for training. To consider the spatial information of the material, the test process cuts multiple test subimages from the test set through mirror padding and overlapping cropping; RUNet then classifies the subimages. Finally, it merges the subimage classification results back into the original test image.The aerial image labeling dataset of the National Institute for Research in Digital Science and Technology (Inria, abbreviated from the French Institut national de recherche en sciences et technologies du numérique) was used as well as its configured dataset (called Inria-2) and a dataset from the International Society for Photogrammetry and Remote Sensing (ISPRS). Material classification was performed with RUNet. Moreover, the effects of the mirror padding and overlapping cropping were analyzed, as were the impacts of subimage size on classification performance. The Inria dataset achieved the optimal results; after the morphological optimization of RUNet, the overall intersection over union (IoU) and classification accuracy reached 70.82% and 95.66%, respectively. Regarding the Inria-2 dataset, the IoU and accuracy were 75.5% and 95.71%, respectively, after classification refinement. Although the overall IoU and accuracy were 0.46% and 0.04% lower than those of the improved fully convolutional network, the training time of the RUNet model was approximately 10.6 h shorter. In the ISPRS dataset experiment, the overall accuracy of the combined multispectral, NDVI, and DSM images reached 89.71%, surpassing that of the RGB images. NIR and DSM provide more information on material features, reducing the likelihood of misclassification caused by similar features (e.g., in color, shape, or texture) in RGB images. Overall, RUNet outperformed the other models in the material classification of remote sensing images. The present findings indicate that it has potential for application in land use monitoring and disaster assessment as well as in model construction for simulation systems.     

Multispectral image denoising with optimized vector non-local mean filter

Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with additive white Gaussian noise. PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity.