Research on assessing compression quality taking into account the space-borne remote sensing images

According to the remote sensing image characteristics,a set of optimized compression quality assessment methods is proposed on the basis of generating simulative images.Firstly,a means is put forward that generates simulative images by scanning aerial films taking into account the space-borne remote sensing camera characteristics(including pixel resolution,histogram dynamic range and quantization).In the course of compression quality assessment,the objective assessment considers images texture changes and mutual relationship between simulative images and decompressed images,while the synthesized estimation factor(SEF) is brought out innovatively for the first time.Subjective assessment adopts a display setup — 0.5mm/pixel,which considers human visual characteristic and mainstream monitor.The set of methods are applied in compression plan design of panchromatic camera loaded on ZY-1-02 C satellite.Through systematic and comprehensive assessment,simulation results show that image compression quality with the compression ratio of 4:1 can meet the remote sensins aDDlication reauirements.

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.

No-Reference Stereo Image Quality Assessment Based on Transfer Learning

In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left view or right view.In this paper,we transfer the 2D image quality evaluation model to the stereo image quality evaluation,and this method solves the first problem;use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem.At the same time,the input image is preprocessed by phase congruency transformation,which further improves the performance of the algorithm.The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers.The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition.

A multimodal dense convolution network for blind image quality assessment

Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Remote sensing image fusion: an update in the context of Digital Earth

Remote sensing image fusion has come a long way from research experiments to an operational image processing technology.Having established a framework for image fusion at the end of the 90s,we now provide an overview on the advances in image fusion during the past 15 years.Assembling information about new remote sensing image fusion techniques,recent technical developments and their influence on image fusion,international societies and working groups,and new journals and publications,we provide insight into new trends.It becomes clear that image fusion facilitates remote sensing image exploitation.It aims at achieving better and more reliable information to better understand complex Earth systems.The numerous publications during the last decade show that remote sensing image fusion is a well-established research field.The experiences gained foster other technological developments in terms of sensor configuration and data exploitation.Multi-modal data usage enables the implementation of the concept of Digital Earth.In order to advance in this respect,we recommend that updated guidelines and a set of commonly accepted quality assessment criteria are needed in image fusion.

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

压缩感知不同加速因子对心脏磁共振电影序列成像质量的影响

目的 探讨不同加速因子的压缩感知(CS)技术对心脏磁共振电影序列图像质量的影响及临床应用的可行性。资料与方法 前瞻性招募合肥市第一人民医院2021年1—7月40名健康志愿者进行心脏磁共振成像,扫描方案共4组(采用SENSE 2及加速因子分别为3、4、8的CS心脏电影序列),每组成像方案包括四腔心、左心室短轴、左心室两腔心及三腔心序列,对比4种方案的图像质量主观评分、左心室心功能及16节段心肌厚度。结果 以SENSE 2图像为标准评分(5分),CS3、CS4评分均在3分以上,CS8评分均在3分及以下,其中CS3与SENSE 2序列的四腔心及左心室短轴图像质量主观评分差异无统计学意义(P均>0.05);左心室两腔心及三腔心各序列组图像质量主观评分差异均有统计学意义(P均<0.05)。4种成像方案的左心室心功能(左心室射血分数、左心室收缩末期容积、左心室舒张末期容积、左心室每搏输出量、左心室舒张末期心肌质量)(F=0.027、0.182、0.057、0.140、0.545)与心肌厚度(F=0.052~7.366)各组间差异均无统计学意义(P均>0.05)。结论 基于CS技术的心脏电影序列具有良好的应用前景。随着加速因子的增加,扫描时间逐渐减少,相应的图像质量降低,而当加速因子为4(与常规电影序列相比扫描时间减少50%)时,仍能够准确进行左心室心功能及心肌厚度测量,且图像质量基本满足诊断需求。

基于在轨MTF测试的定量图像质量提升方法

针对TDI推扫型光学遥感载荷在轨工作时可能出现的垂轨或沿轨方向图像质量下降现象,提出一种基于在轨MTF测试的单方向可定量图像质量提升方法。首先,采用刃边法测试得到沿轨和垂轨两个方向的MTF曲线,选取从零频到奈奎斯特频率范围内的n个不同频率点,根据MTF目标值与实测值的比值确定各选定频率点的MTF提升倍数,依据频域响应特性并以提升倍数作为频域响应幅值构建空域卷积函数,同时结合在轨实测信噪比(Signal to Noise Ratio,SNR)构建抑噪函数,在确保SNR的前提下实现的MTF的定量提升。根据在轨试验验证结果表明,按照所提方法进行图像质量提升,在噪声抑制阈值0.5 dB范围内,垂轨方向MTF基本不变,沿轨方向奈奎斯特频率点的MTF提升2.51倍,获取的图像清晰度提升8.33%,证明该方法可有效实现定量图像质量提升。

对象级时空融合模型在NDVI和LST的应用分析—以大理地区为例

过去几十年中,时空融合技术为实现长时间序列观测提供了一种经济高效的方法,但此类方法对结构信息的保留能力较弱,同时计算的效率也比较低。该研究对比分析主流时空融合方法与对象级(Object Level,OL)时空融合方法,在归一化植被指数(NDVI)和地表温度(LST)的融合效果和融合效率的差异。文章以大理地区作为研究区,使用9种时空融合方法对Landsat和MODIS数据做融合处理,通过目视判别和统计分析,评估其在时空模拟效果和计算效率上的差异。实验表明:1)OL-FSDAF2.0(Object Level-Flexible Spatiotemporal Data Fusion 2.0)相较于其他时空融合方法更好地恢复了地表真实信息和结构信息;2)对象级时空融合方法在计算效率方面比其余像素级时空融合方法提高20.70倍;3)对象级时空融合方法对地物时间动态特征细节的捕捉能力均比像素级时空融合方法高。总的来说,对象级时空融合方法具有较高的计算效率和更精确的融合效果,其中,OL-FSDAF2.0在复杂地表区域与模拟地表覆盖动态变化中表现较好。

基于连续变化检测和分类算法的动态遥感生态指数构建

沿海地区经济社会高速发展,是生态环境变化的焦点区域。然而,沿海地区云雨天气频发,遥感信息获取能力受限,导致遥感生态质量指数(RSEI)评价结果受成像日期变化而波动,可比性较差。针对以上问题,研究利用连续变化检测和分类(CCDC)算法构建时间序列模型,通过合成任意时刻影像、重构遥感生态指数以及改进指数归一化方式,研发了一种动态遥感生态指数(DRSEI),细化了RSEI在区域生态质量监测的时间尺度,并应用于沿海城市宁波生态质量时空变化监测。结果表明:(1)RSEI对时间差异较为敏感,当影像年内成像时间相差逾1个月,RSEI差异可达0.147,这种差异会对长期生态质量动态监测的稳定性和准确性造成影响。(2)基于合成影像的DRSEI平均绝对偏差为0.097,接近成像时间相差半个月的RSEI差异(0.072),误差相对较小,一定程度上减小了真实影像时相差异引起的误差。(3)DRSEI能够表征任意时刻生态质量,通过年际(1986—2019年)和半月际(2019年)DRSEI分析揭示了宁波市生态质量总体下降趋势和时空异质性加剧过程。具体地,1986—2019年宁波市南部和西部森林区域的DRSEI持续上升,而近郊农田快速转化为建成区导致DRSEI不断下降。研究提出的DRSEI能够精确描述区域生态质量变化趋势,准确定位生态质量变化转折点,有望服务海岸带地区的生态质量定期监测与评估工作,支持沿海城市高质量发展与生态环境保护。

利用改进型遥感生态指数的岩溶区生态环境质量评价

本文选取绿度(NDMVI)、湿度(Wet)、石漠化(RDI)和热度(LST)4个指数,构建了改进型遥感生态指数(IRSEI),以实现县域岩溶地区的生态环境质量评价。基于2003与2023年的县域尺度遥感影像与其他地理信息数据,通过若干方面定量对比IRSEI与RSEI指数。结果表明,IRSEI包含了更丰富的山地植被与石漠化状况信息,能够更好地表征和评价岩溶地区生态环境质量,适合作为岩溶地区生态环境质量评价的定量指标。研究区在2003—2023年间,生态环境质量改善和退化情况均有发生,但生态环境质量总体下降了7.46%,生态等级由良变为中等。从空间分布来看,罗平县研究区生态质量差的区域主要分布在北部,生态环境质量优的区域主要分布在中部。

基于遥感的区域生态环境质量评价研究综述

当前,卫星遥感对地观测技术以其宏观、快速、实时的优势在生态环境质量评价领域得到广泛应用。本研究梳理了国内外区域生态环境质量评价的发展,介绍了评价程序中指标权重的确定方法及生态环境质量评价方法的选择,并对其中的指数法展开延伸讨论。研究表明,基于遥感的区域生态环境质量评价方法主要有模糊综合评价法、物元分析评价法、指数评价法等,其中综合指数评价法应用最广。评价过程中指标体系权重确定主要有主成分分析法、层次分析法及熵值法,其中主成分分析法因不受人为主观因素影响最为客观。

墨江哈尼族自治县生态环境质量时空变化及影响因子

调查区域生态环境质量时空变化有助于对生态环境的保护和改善提供指导和进行协调。基于2005—2020年Landsat遥感数据,运用遥感生态指数(RSEI)和地理探测器模型,探究墨江哈尼族自治县生态环境质量时空变化及影响因子。结果表明:(1)研究区2005—2020年遥感生态指数均值分别为0.5689、0.5824、0.5866、0.5985,呈现出逐年增长的趋势;(2)2020年研究区生境质量等级为优和良的面积超过一半,但流域生态环境治理仍需进一步改进;(3)研究区生态环境质量变化受多因子相互作用影响,干度因子与热度因子对生态环境质量空间分布的解释力最大。

光学遥感卫星精密敏捷成像控制技术综述

从控制的角度综述了多功能、高质量成像需求下,超稳定、超精确、超敏捷高分辨率光学遥感卫星精密敏捷控制的关键技术和研究进展。以卫星成像与姿态控制系统性能的关系为基础,从姿态机动能力与多功能成像、姿态确定精度与成像质量和姿态控制精度与成像质量三个方面论述了精密敏捷控制技术对光学遥感卫星成像的重要性。在此基础上,结合光学遥感卫星姿态控制系统的组成及原理,分析了影响姿态控制精度和姿态机动能力的主要因素,对高精度姿态确定、精密姿态控制和敏捷姿态控制三个关键技术方向进行了讨论和总结。最后,综合当前技术的发展趋势,围绕一体化成像控制的设计思想展望了精密敏捷控制技术未来的发展趋势,为实现高分辨率光学遥感卫星多功能、高质量成像控制提供了理论依据和工程参考。

基于Landsat 8数据的广东石马河流域水质参数反演研究

针对河流水域水质进行高效快速监测的需要,本文以2021年2月广东石马河流域水体为研究对象,利用Landsat 8的OLI遥感影像对试验区域的环城河化学需氧量进行遥感反演研究。本文首先对实测水质数据和相应卫星影像数据波段值进行相关性分析,然后选取相关性最高的遥感波段值对水质进行回归分析,最后建立了适用于石马河流域的三次多项式反演模型。经反演预测结果与现场实测水质数据对比,试验结果表明所建立的水质反演模型能对石马河流域水质化学需氧量浓度进行高效快速监测。

不同上下文比例对损毁建筑遥感场景图片样本集构建的影响

基于深度学习的遥感影像场景分析是震后进行损毁评估的重要手段。在损毁建筑影像资源相对稀缺的情况下,构建高质量的遥感场景图片样本集,对提高场景识别和分类精度具有重要意义。作为遥感分析的重要参考依据,上下文信息在场景图片中所占比例是影响样本集构建效果的一个关键因素。目前,样本集构建方法中缺乏对上下文信息合适比例的探索。该文以构建高质量样本集为目标,设计一种调整场景图片上下文信息比例的方法,研究不同上下文信息占比对场景样本集构建的影响,探索上下文信息比例的最佳设置范围。文章构建6组不同上下文信息占比的场景图片样本集,使用5种经典卷积神经网络(convolutional neural network,CNN)进行训练和测试,并依次对每个模型的分类结果和不同上下文信息的分类结果进行分析。研究表明,当上下文信息占比为80%时,CNN网络达到了最佳的分类准确率(92.22%),当上下文信息占比为95%时,则降到89.03%;在所有的CNN模型中,GoogLeNet的分类表现最好,平均准确率达到93.13%。该研究可以找到场景样本集中合理的上下文信息比例设置范围,有效提高遥感场景图片分类的准确率,为损毁建筑遥感场景图片样本集的制作提供指导。