Rapid identification of landslide,collapse and crack based on low-altitude remote sensing image of UAV

Landslides,collapses and cracks are the main types of geological hazards,which threaten the safety of human life and property at all times.In emergency surveying and mapping,it is timeconsuming and laborious to use the method of field artificial investigation and recognition and using satellite image to identify ground hazards,there are some problems,such as time lag,low resolution,and difficult to select the map on demand.In this paper,a10 cm per pixel resolution photogrammetry of a geological hazard-prone area of Taohuagou,Shanxi Province,China is carried out by DJ 4 UAV.The digital orthophoto model(DOM),digital surface model(DSM) and three-dimensional point cloud model(3 DPCM) are generated in this region.The method of visual interpretation of cracks based on DOM(as main)-3 DPCM(as auxiliary) and landslide and collapse based on 3 DPCM(as main)-DOM and DSM(as auxiliary) are proposed.Based on the low altitude remote sensing image of UAV,the shape characteristics,geological characteristics and distribution of the identified hazards are analyzed.The results show that using UAV low altitude remote sensing image,the method of combination of main and auxiliary data can quickly and accurately identify landslide,collapse and crack,the accuracy of crack identification is 93%,and the accuracy of landslide and collapse identification is 100%.It mainly occurs in silty clay and mudstone geology and is greatly affected by slope foot excavation.This study can play a great role in the recognition of sudden hazards by low altitude remote sensing images of UAV.

Analysis of color distortion and optimum fusion for remote sensing images using the statistical property of wavelet decomposition

IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can＇t be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component Ⅰ is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component Ⅰ is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.

Low Complexity Encoder with Multilabel Classification and Image Captioning Model

Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT,which are commonly employed in diverse application areas, especially forcapturing remote sensing (RS) images. At the same time, the interpretationof the captured RS image also plays a crucial issue, which can be addressedby the multi-label classification and Computational Linguistics based imagecaptioning techniques. To achieve this, this paper presents an efficient lowcomplexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves thelow complexity encoder using the Neighborhood Correlation Sequence (NCS)with a burrows wheeler transform (BWT) technique called LCE-BWT forencoding the RS images captured by the UAV. The application of NCS greatlyreduces the computation complexity and requires fewer resources for imagetransmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presentedto determine the multiple class labels of the RS image, shows the novelty ofthe work. Finally, the Computational Linguistics based Bidirectional EncoderRepresentations from Transformers (BERT) technique is applied for imagecaptioning, to provide a proficient textual description of the RS image. Theperformance of the presented technique is tested using the UCM dataset. Thesimulation outcome implied that the presented model has obtained effectivecompression performance, reconstructed image quality, classification results,and image captioning outcome.

基于改进DeeplabV3+的遥感图像道路分割模型

针对遥感图像道路分割边界模糊和遮挡难以区分的问题,提出了基于改进DeeplabV3+的遥感图像道路分割模型。该模型在主干网络中引入MobileNetV3和高效通道注意力机制(ECA),减少了参数量并关注连续的道路特征信息。在解码过程中采用多级上采样,增强了编码器和解码器之间的紧密连接,全面保留了细节信息。同时,在ASPP模块中采用深度可分离膨胀卷积DS-ASPP,显著减少了参数量。实验结果表明,该模型在Massachusetts Roads数据集上的交并比达到了83.71%,准确率达到了93.71%,分割精度最优,模型参数量为55.57×10^(6),能够有效地避免边界模糊和遮挡导致的错漏检问题,在遥感道路分割中提高了精度和速度。

青藏高原南部玛不错湖面变化及其对气候环境的指示

青藏高原藏南谷地中部的玛不错湖位于印度夏季风和西风影响区内,对气候变化响应敏感。不同年份相同时相的遥感影像反映了湖面的变化特征,是探究区域气候变化的重要对象。湖岸堤和湖成阶地沉积物记录了湖面水位变化的历史,可帮助认识区域古气候的变化和定量重建湖面波动。本文运用ArcGIS遥感解译、AMS ^( 14)C测年和DEM等方法确定玛不错北岸湖岸堤的高程和湖岸阶地的年代,结合湖成阶地剖面的沉积序列指示的湖面变化过程,重建晚更新世以来玛不错湖面的变化过程。S_(7)-S_(4)湖岸堤阶段,14256~13984 a BP之前,玛不错与其南侧的嘎拉错、多庆错为一体,是一个统一的大湖。S_(7)→S_(4),湖平面总体上呈逐渐下降的趋势,玛不错与多庆错、嘎拉错先后分离形成独立湖泊。S_(4)→S_(3)阶段,湖面逐渐上涨,分离的玛不错与嘎拉错重新连为一体,但这个过程持续时间比较短暂。S_(3)-S_(1)阶段,14256~13984 a BP之后,玛不错成为一个独立的湖泊。S_(3)→S_(1)阶段,湖面整体上呈逐渐下降的趋势。综合来看,晚更新世以来玛不错湖面经历了高→低→高→低的变化过程,湖面升降变化主要受区域大气降水和冰川融水的控制,反映了印度季风的强弱变化和全球气候的变化。近十年来遥感解译的湖面变化显示,玛不错2013-2015年期间呈萎缩状态,2016-2018年期间呈扩张状态,反映近年来青藏高原藏南谷地中部的气候有向暖湿化发展的趋势。该认识对于全球气候变暖背景下青藏高原气候环境变化趋势研究领域提供了新的参考。

基于中低分辨率卫星影像中舰船目标识别方法

目前,中低分辨率成像侦察卫星已经成为远距离情报保障的重要手段之一。然而,中低分辨率成像侦察卫星对海上机动舰船的侦察存在目标检测、识别难等问题,大大影响了情报使用效益。针对上述问题,提出基于霍夫变换的舰船开尔文尾迹提取方法,用于匹配并识别舰船型号。通过仿真得出不同型号舰船的开尔文尾迹尖头波平均距离,可以有效的识别出真实遥感影像中舰船属性,与实际影像数据对比分析,证明上述算法具有一定可行性。因此,基于霍夫变换的舰船开尔文尾迹提取方法可以在一定条件下基本实现目标舰船型号的识别,提升中低分辨率成像侦察卫星对海上机动舰船目标的识别准确率。

改进Faster R-CNN的遥感图像小目标检测算法

遥感图像目标检测是目标检测领域的一个关键问题,目前利用深度学习检测目标的算法大多在单向特征融合过程中添加注意力机制,一视同仁地去增强各类型的目标,并不能突出小目标。为了取得更好的检测效果,通过引入非对称高低层调制机制,构造兼顾低层细节信息和高层语义信息的特征图,以达到增强小目标特征检测的目的;同时使用DIoU损失函数代替原算法SmoothL1损失函数以提升算法检测精度与收敛速度;并且在感兴趣区域分类任务中引入灵活上下文信息以提高小目标分类准确性。实验结果表明,该算法在DIOR和NWPU VHR-10数据集上均取得了良好的表现。

多光谱遥感影像在长江水位监测中的应用

人类社会生活、生产活动与江河水位变化密切相关。河道水位监测系统可以实时掌握河道水位的变化情况,是科学预警水情隐患、保障港口及航运安全、提升防汛抗旱能力的重要手段。本文对长江局部水域面积与水位观测值之间的相关性进行了研究,并建立了回归方程;最终利用多光谱遥感影像提取出长江水域面积,并应用该回归方程进行长江水位的推算。结果表明,该方法能够为江河、湖泊、水库等水体的水位监测提供一种简便可行的估算方法。

基于RSSD的遥感图像目标检测算法

针对SSD算法检测遥感图像目标时存在容易漏检且检测精度低的问题,提出基于残差SSD网络的遥感图像目标检测算法。该算法在SSD网络结构的基础上,将基准网络模型VGG替换为残差网络模型ResNet-50,通过增加网络深度,充分提取遥感图像小目标数据集的底层特征,引入注意力模块,使感受野更关注目标特征,增强低层网络的信息表征能力,采用特征金字塔融合方法融合网络结构的高层语义特征和低层视觉特征,增强检测目标的定位能力。实验结果表明,该算法增强了复杂背景的干扰抑制性,提高了小目标的检测精度,比传统的SSD算法具有更强的检测性能。

基于改进粗糙集的低照度遥感图像对比度增强方法

为解决图像增强后存在的噪声问题,文章提出基于改进粗糙集的低照度遥感图像对比度增强方法。该方法先基于改进粗糙集进行低照度遥感图像预处理,后自适应调整低照度遥感图像,采用提高饱和度、恢复色彩等操作,完成对图像的对比度增强处理。实验结果表明,文章所提方法的应用使图像的对比度指标得到了显著提升,应用效果更优。

基于Transformer的遥感影像弱监督语义分割

针对遥感影像语义分割任务场景复杂、标注成本高的问题,提出一种基于Transformer的端到端图像级标签弱监督语义分割网络。首先,通过多类别标记编码模块,提高类别激活映射图的精确度和细粒度;其次,通过亲和伪标签生成模块进一步完成亲和关系表征的细化,生成高精度亲和伪标签作为分割监督信息,从而提高弱监督网络的能力;同时,设计混合标签数据增强模块强化遥感数据构成;最后,给出融合亲和损失的混合损失函数,强化网络的学习性能。在ISAID数据集上的实验结果表明,该模型在使用图像级标签下分割结果的mIoU达到38.836%,较对照网络表现出更好的鲁棒性和可靠性,在遥感影像弱监督语义分割领域具有较高的应用价值。

长江中游干流河段洲滩民垸行蓄洪能力研究

长江中下游洲滩民垸具有巨大行蓄洪能力,对保障重点地区防洪安全起重要作用。但是,洲滩民垸点多面广,地形数据缺乏,无法充分利用并有效构建数学模型分析行蓄洪作用。基于长江中下游高分辨率遥感影像数据,构建长江中游干流河段洲滩民垸数字高程模型,以生产水位-面积-容积关系成果,分区分类研究洲滩民垸的行蓄洪能力。结果表明:长江中游干流洲滩民垸行蓄洪能力强,但空间分布差异大,其中,荆江河段洲滩民垸面积及蓄洪容积占比最高,在面临大洪水时调度运用多。研究成果能够直观有效呈现长江中游干流洲滩民垸的行蓄洪能力,为洲滩民垸的调度运用打牢数据基础,为科学准确制定防洪决策提供有力支持。

1988-2021年珠江口内伶仃洋滩涂结构遥感监测研究

【目的】为揭示沿海城市周期性潮位影响下滩涂范围与地貌结构,【方法】基于GEE平台搜集Landsat影像数据,通过潮位影像融合算法构建珠江口内伶仃洋1988—2021年逐3 a海岸带滩涂结构数据集,辨析滩涂结构时空演变过程及其与海岸带工程的关系。【结果】结果显示,截至2021年,内伶仃洋(含深圳湾北部)共有滩涂33.7 km^(2),主要分布于深圳湾北部、龙穴岛、蕉门至横门海岸带,深圳段占比42%,中山段占比25%,广州段和东莞段面积狭小。内伶仃洋滩涂整体呈现出波动下降趋势,且出现频率逐渐降低,12期滩涂总面积累计减少65.39 km^(2),潮上带从15.67 km^(2)减少至4.25 km^(2),潮间带37.32 km^(2)减少至5.44 km^(2),潮下带则46.14 km^(2)减少至24.04 km^(2),整体上滩涂潮间带和潮上带缩减面积最大。【结论】长序列影像序列可有效识别潮上带和潮间带的动态转化,填海造陆工程扩张导致整体滩涂的永久性占用,而生态修复工程则有效维持了部分滩涂的面积和分布范围。

More efficient ground truth ROI image coding technique: implementation and wavelet based application analysis

In this paper, more efficient, low-complexity and reliable region of interest （ROI） image codec for compressing smooth low texture remote sensing images is proposed. We explore the efficiency of the modified RO! codec with respect to the selected set of convenient wavelet filters, which is a novel method. Such ROI coding experiment analysis representing low bit rate lossy to high quality lossless reconstruction with timing analysis is useful for improving remote sensing ground truth surveillance efficiency in terms of time and quality. The subjective [i.e. fair, five observer （HVS） evaluations using enhanced 3D picture view Hyper memory display technology] and the objective results revealed that for faster ground truth ROI coding applications, the Symlet-4 adaptation performs better than Biorthogonal 4.4 and Biorthogonal 6.8. However, the discrete Meyer wavelet adaptation is the best solution for delayed ROI image reconstructions.

基于PCA信息熵特征融合的遥感影像变化检测

为提高遥感影像变化检测的准确率,本研究结合遥感影像中丰富的纹理信息与邻域信息,引入信息论中信息熵的概念,从特征融合的角度提出了一种基于主成分分析(PCA)信息熵融合纹理特征与邻域特征的遥感影像变化检测方法。首先,通过灰度共生矩阵提取遥感影像的纹理特征,邻域对数比算子提取遥感影像的邻域特征,然后,利用PCA将纹理特征与邻域特征进行特征融合获得主特征信息。其次,计算每个主特征的信息熵,以熵权法为各个主特征分别赋予权重,获得融合后的差异图。最后,利用模糊C均值(FCM)聚类将融合差异图划分为两类,得到变化检测结果。实验结果表明,该方法能够充分利用纹理特征与邻域特征,减小斑点噪声带来的影响,显著提高遥感影像变化检测的精度。

顾及纹理特征差异性的遥感影像数据分区域多密级加密方法

针对高分辨率遥感影像在网络传输、数据分享及应用过程中的安全性问题,本文从高分辨率遥感影像自身特点出发,提出了一种顾及纹理复杂度的遥感影像分区域多密级加密算法。首先,对大数据量的遥感影像进行分块处理,并将每个子图像块按照纹理特征的复杂度划分为简单、中等和复杂类型;然后,针对不同类型的子图像块采用不同维度的混沌系统分别加密得到密文块;最后,将密文块进行关联拼接,得到整体加密后的密文图像。通过采用真实遥感影像的试验分析结果表明,本文方法能够很好地实现遥感影像的信息隐藏,并且可以兼顾加密的安全性和时间复杂度,在大数据时代面向大众化应用的高分辨率遥感影像数据共享和图像处理领域具有潜在应用价值。

微光条件下遥感器像移补偿机构设计及控制方法

夜间成像时,遥感成像地面目标的辐亮度可低至白天的10–6,在提升弱信号获取能力的各种措施中,通过长曝光时间提升信噪比是一种直接而有效的解决方式,但是在低轨遥感任务中其实现却受到极大限制。为完成探测目标凝视,文章提出通过光路中的像移补偿机构实现光轴稳定的解决方案,补偿了相机相面与地物的相对运动,在曝光时间内使探测器相面景物与地物景物保持相对静止,从而实现灵活微光成像模式;具体阐述了微光条件下遥感器像移补偿机构的设计和控制方法,对其原理及设计进行了详细分析,并进行仿真及试验验证,结果表明所设计的像移补偿系统可将图像抖动补偿至0.2像元之内,为低轨遥感微光成像系统提供了优异的解决途径。

基于高分一号卫星数据的库区淹没频率分析方法

为分析水库水位变化对消落带生态结构的影响,提高淹没频率计算精度,提出一种基于高分一号卫星数据的淹频率分析方法。该方法通过对高分一号WFV影像数据进行预处理并多次迭代提取缓冲区的水体信息,结合地理配准和计算得到淹没频率空间分布特征,通过以此模拟的水体分布结果的精度验证淹没频率的可靠性。三河口水库实例验证结果表明:提出的水库淹没频率分析方法较准确地反映了水体信息的时空变化特征,模拟的水体信息结果总体精度和Kappa系数均值分别达到86.5%和73%,较准确地还原了水库蓄水阶段的水体变化,提高了水体淹没频率计算的精度。

Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion

Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.