Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Super-resolution Restoration of Remote-sensing Images

A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that super-resolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function (PSF) value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

New Mathematical Model Based on Affine ransformation for Remote Sensing Image with High Resolution

This paper calculates the parameters of image position and orientation,proposes a mathematical model and adopts a new method with three steps of transformations based on parallel ray projection.Every step of the model is strict,and the map function of each transformation is the first order polynomials and other simple function.The final calculation of the parameters is for the linear equations with good status.As a result,the problem of the relativity of image parameter calculation is solved completely.Some experiments are carried out.

Multiresolution Fusion of Remote Sensing Images Based on Resolution Degradation Model

A new method based on resolution degradation model is proposed to improve both spatial and spectral quality of the synthetic images. Some ETM+ panchromatic and multispectral images are used to assess the new method. Its spatial and spectral effects are evaluated by qualitative and quantitative measures and the results are compared with those of IHS, PCA, Brovey, OWT(Orthogonal Wavelet Transform) and RWT(Redundant Wavelet Transform). The results show that the new method can keep almost the same spatial resolution as the panchromatic images, and the spectral effect of the new method is as good as those of wavelet-based methods.

多尺度特征融合的Transformer遥感影像超分辨率重建

针对现有遥感影像超分辨率重建算法,在处理复杂场景时,存在无法充分提取和利用特征,且计算复杂度高的问题,提出一种多尺度特征融合的Transformer遥感影像超分辨率重建网络模型.该模型引入了多尺度残差Swin Transformer模块,在充分提取特征的同时,减少用于提取浅层特征的模块冗余;建立了一个特征细化融合模块,可以充分提取图像特征来提高网络性能.基于UC Merced Land Use公开数据集进行实验,结果表明:提出的模型所需参数数量仅为目前主流超分辨率重建方法 EDSR模型的61.6%,重建结果在不同尺度下的峰值信噪比和结构相似度相对EDSR分别平均提高了0.82 dB和0.024.通过对比分析,证明本文提出的模型在提高图像质量的同时,有效地减少了网络参数冗余,可明显提高重建图像质量,满足高分辨率遥感影像处理需要.

基于Swin Transformer的遥感图像超分辨率重建

由于遥感图像中的物体具有不确定性,同时不同图像之间的特征信息差异较大,导致现有超分辨率方法重建效果差,因此本文提出一种结合Swin Transformer和N-gram模型的NG-MAT模型来实现遥感图像超分辨率.首先,在原始Transformer计算自注意力的分支上并联多注意力模块,用于提取全局特征信息来激活更多像素.其次,将自然语言处理领域的N-gram模型应用到图像处理领域,用三元N-gram模型来加强窗口之间的信息交互.本文提出的方法在所选取的数据集上,峰值信噪比在放大因子为2、3、4时达到了34.68 dB、31.03 dB、28.99 dB,结构相似度在放大因子为2、3、4时达到了0.9266、0.8444、0.7734,实验结果表明,本文提出的方法各个指标都优于其他同类方法.

一种耦合DeepLab与Transformer的农作物种植类型遥感精细分类方法

如何精细遥感监测复杂的不同类型农田作物种植情况,是智慧农业农村领域实现农耕面积调查与农作物估产的关键。目前的高分辨率影像的作物种植像素级语义分割中,深度卷积神经网络难以兼顾空间多尺度全局特征和局部细节特征,从而导致各类农田地块之间边界轮廓模糊和同类农田区域内部完整性不高等问题。针对这些不足,本文提出了一种耦合DeepLabv3+和Transformer编码器的双分支并行特征融合网络FDTNet,以实现农作物种植类型的精细遥感监测。首先,在FDTNet中并行嵌入DeepLabv3+和Transformer分别捕获农田影像的局部特征和全局特征;其次,应用耦合注意力融合模块CAFM有效融合两者的特征;然后,在解码器阶段应用卷积注意力模块CBAM增强卷积层有效特征的权重;最后,采用渐进式多层特征融合策略将编码器和解码器中的有效特征全面融合并输出特征图,以实现晚稻、中稻、藕田、菜地和大棚的高精度分类识别。为了验证FDTNet网络模型在高分辨率作物分类应用的有效性,本文选择不同高分辨率的Yuhu数据集和Zhejiang数据集验证,mIoU分别达到74.7%和81.4%。相比于已有的UNet、DeepLabv3、DeepLabv3+、ResT和Res-Swin等深度学习方法,FDTNet的mIoU可分别高2.2%和3.6%。结果表明,FDTNet在纹理单一、大样本量,以及纹理多样、小样本量的两类农田场景中同时表现出优于对比方法的性能,具有较全面的多类别农作物有效特征提取能力。

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

Influences of Atmospheric Turbulence on Image Resolution of Airborne and Space-Borne Optical Remote Sensing System

A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.

Super-resolution filtered ghost imaging with compressed sensing

A filtered ghost imaging(GI)protocol is proposed that enables the Rayleigh diffraction limit to be exceeded in an intensity correlation system;a super-resolution reconstructed image is achieved by low-pass filtering of the measured intensities.In a lensless GI experiment performed with spatial bandpass filtering,the spatial resolution can exceed the Rayleigh diffraction bound by more than a factor of 10.The resolution depends on the bandwidth of the filter,and the relationship between the two is investigated and discussed.In combination with compressed sensing programming,not only high resolution can be maintained but also image quality can be improved,while a much lower sampling number is sufficient.

Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

基于CNN-Transformer半监督交叉学习的遥感图像场景分类方法

随着深度学习技术的发展,基于卷积神经网络(CNN)和Transformer的深度学习方法在全监督遥感图像场景分类任务中得到了广泛的关注与研究.然而,如何在标注样本有限的情况下实现良好的分类性能仍然具有挑战.考虑到CNN和Transformer在深度特征提取方式上的差异,提出一种CNN和Transformer半监督交叉学习的遥感图像场景分类方法(SCL-CTNet),通过构建CNN和Transformer输出的一致性约束,更好地提取未标记数据中的信息,指导模型训练.半监督交叉学习方法将弱增强图像在一个网络上的输出作为伪标签用于监督强增强图像在另一个网络的预测结果,充分利用未标记样本的局部-全局信息,鼓励两个网络对相同输入图像预测间的一致性,提高模型泛化性.使用自适应阈值筛选伪标签,提高伪标签可靠性.在AID和NWPU-RESISC45数据集上的实验结果证明了所提出方法的有效性.

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.

Responses of Alpine Wetlands to Climate Changes on the Qinghai-Tibetan Plateau Based on Remote Sensing

The alpine wetlands in QTP(Qinghai-Tibetan Plateau)have been profoundly impacted along with global climate changes.We employ satellite datasets and climate data to explore the relationships between alpine wetlands and climate changes based on remote sensing data.Results show that:1)the wetland NDVI(Normalized Difference Vegetation Index)and GPP(Gross Primary Production)were more sensitive to air temperature than to precipitation rate.The wetland ET(evapotranspiration)across alpine wetlands was greatly correlated with precipitation rate.2)Alpine wetlands responses to climate changes varied spatially and temporally due to different geographic environments,variety of wetland formation and human disturbances.3)The vegetation responses of the Zoige wetland was the most noticeable and related to the temperature,while the GPP and NDVI of the Qiangtang Plateau and Gyaring-Ngoring Lake were significantly correlated with both temperature and precipitation.4)ET in the Zoige wetland showed a significantly positive trend,while ET in Maidika wetland and the Qiangtang plateau showed a negative trend,implying wetland degradation in those two wetland regions.The complexities of the impacts of climate changes on alpine wetlands indicate the necessity of further study to understand and conserve alpine wetland ecosystems.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

基于改进RoI Transformer的遥感图像多尺度旋转目标检测

旋转目标检测是遥感图像处理领域中的重要任务,其存在的目标尺度变化大和目标方向任意等问题给自动目标检测带来了挑战。针对上述问题,提出了一种改进的RoI Transformer旋转目标检测框架:首先,利用RoI Transformer检测框架获取旋转的感兴趣区域特征(rotated region of interest,RRoI)用于鲁棒的几何特征提取;其次,在检测器中引入高分辨率网络(high-resolution network,HRNet)提取多分辨率特征图,在保持高分辨率特征同时适应目标的多尺度变化;最后,引入KLD(Kullback-Leibler divergence)损失,解决旋转目标表示的角度周期性的问题,提高检测方法对任意方向目标的适应性,并通过旋转目标边界框参数的联合优化提升目标定位精度。本文提出的旋转目标检测方法,即HRD-ROI Transformer(HRNet+KLD ROI Transformer),在DOTAv1.0和DIOR-R两个公开数据集上与典型的旋转目标检测方法进行了比较。结果显示:相比于传统的RoI Transformer检测框架,本文方法在DOTAv1.0和DIOR-R数据集上检测结果的mAP(mean-average-precision)分别提高了3.7%和4%。

轻量级重参数化的遥感图像超分辨率重建网络设计

针对当前基于深度学习的遥感图像超分辨率重建模型部署时对硬件要求较高,本文设计了一种轻量级基于重参数化的残差特征遥感图像超分辨率重建网络。首先,采用重参数化方法设计了一种残差局部特征模块,以有效地提取图像局部特征;同时考虑到图像内部出现的相似特征,设计了一个轻量级的全局上下文模块对图像的相似特征进行关联以提升网络的特征表达能力,并通过调整该模块的通道压缩倍数来减少模型的参数量和改善模型的性能;最后,在上采样模块前使用多层特征融合模块聚合所有的深度特征,以产生更全面的特征表示。在UC Merced遥感数据集上进行测试,该算法在遥感图像3倍超分辨率下的参数量为539 K,峰值信噪比为30.01 dB,结构相似性为0.8449,模型的推理时间为0.010 s;而HSENet算法的参数量为5470 K,峰值信噪比为30.00 dB,结构相似性为0.8420,模型的推理时间为0.059 s。实验结果表明,该算法相比HSENet算法,参数量更少,运行速度较快,且峰值信噪比与结构相似性也有一定的提高。在DIV2K自然图像数据集上进行测试,该算法的峰值信噪比和结构相似性相比其他算法也有一定的优势,表明该算法的泛化能力较强。