基于HJ-1B卫星的作物秸秆提取及其焚烧火点判定模式

作物秸秆焚烧产生的气体和颗粒物严重污染大气环境,威胁人类健康,给交通带安全带来隐患,利用遥感技术优势监管秸秆焚烧火点具有重要的现实意义.文中基于HJ-1B卫星CCD多光谱遥感数据和IRS热红外遥感数据,以中国江苏中东部为研究区,开展作物秸秆提取及其焚烧火点判定的一体化研究.根据秸秆的光谱特征研究建立了秸秆乘积指数(SMI),结合其纹理信息可从HJ-1BCCD遥感图像上快速有效的提取出秸秆分布,继之结合修正后的火点探测算法可对HJ-1BIRS遥感数据进行火点提取.在秸秆分布和火点探测结果矢量化的基础上,通过GIS技术进行火点叠置分析,可有效地判定作物秸秆火点分布,同时结合实地调研及与MODIS火点产品比对分析验证评价了本研究方法的可行性和有效性.     

基于J2EE的秸秆火点监测系统设计

秸秆焚烧火点监测在我国受到越来越多的重视,而遥感作为秸秆焚烧火点监测的有力手段也得到越来越多的应用。本文利用J2EE技术,实现了秸秆火点监测系统设计,并详细介绍了系统的设计思想,以及开发过程中的核心算法和主要技术问题。     

秸秆焚烧火点遥感监测算法实现

为了获取华中区域的秸秆焚烧火点空间分布信息,实现对该区域秸秆焚烧的有效管控,以2014年MODIS L1B遥感数据为主要数据源,结合土地利用类型数据,以华中的农田为研究区域,基于增强型上下文火点遥感影像识别方法,充分利用定量遥感的理论知识及地理空间数据抽象库(GDAL)等技术手段,实现了华中区域秸秆焚烧火点的识别。利用中华人民共和国环境保护部发布的全国秸秆焚烧火点日报和MODIS标准火点产品(MYD14)进行空间和定量上的对比分析。研究结果表明,该算法能够有效地进行研究区域的秸秆焚烧火点遥感监测,并且可以依据研究区域的特点进行参数的实时调整,提高了秸秆焚烧火点提取的自动化和工作效率。     

秸秆焚烧遥感监测进展分析与展望

秸秆焚烧是生物质燃烧的重要组成部分,不仅导致秸秆资源浪费,而且还会对环境造成严重危害。传统秸秆焚烧监测方法以人工巡查为主,监测范围受限且人力物力资源耗费大。遥感技术作为新兴的地表信息监测手段,给秸秆焚烧大范围监测带来了发展契机。介绍了遥感技术在秸秆焚烧火点监测、过火面积估算和焚烧迹地监测3个方面的基本原理、监测方法和研究进展,并分析了遥感技术在秸秆焚烧监测应用中存在的不足。在此基础上,从多源数据融合互补、监测方法优化集成、监测信息深入挖掘和时空信息决策服务等4个方面对秸秆焚烧遥感监测的未来发展进行了展望。     

基于MODIS的秸秆焚烧火点识别原理及算法IDL实现

秸秆焚烧容易污染空气,影响交通。常规秸秆焚烧监测难度较大,而卫星遥感数据具有覆盖面广,时效性强、分辨率高等优点,有利于秸秆焚烧监测工作的进展。本文将介绍如何使用MODIS数据提取秸秆焚烧火点的算法。在算法实现上采用交互式数据语言IDL（Interactive Data Language）进行实现,大大缩短了人机交互时间,提高了秸秆焚烧点自动提取的响应速度,从而提高工作效率,促进秸秆焚烧监测工作的开展。     

基于HJ-1卫星CCD遥感数据的作物秸秆光谱识别模型与提取方法研究

作物秸秆信息的准确提取对于农业可持续发展与秸秆焚烧火点的探测有重要意义。本文在野外作物秸秆光谱采集与光谱特征分析的基础上,以江苏省扬州市区为典型研究区,依照HJ卫星CCD相机多光谱遥感波段进行光谱重采样,并结合HJ卫星CCD遥感数据,建立了作物秸秆光谱识别乘积指数(SMI),最后通过改进PSO最大类间方差算法对SMI影像进行"动态-全局"阈值分割,实现了作物秸秆信息的有效提取。     

物联网视觉中森林小距离火点定位算法仿真

研究物联网视觉中森林小距离火点定位优化问题.在森林火点定位中,物联网视觉遥感图像采集规范不同,图像特征存在质量差异,森林内不同区域、不同质量的图像在经过统一几何校正后,获取的定位特征残差扩大.传统算法应用转换后的图像进行定位,会导致火点对地定位精度大幅下降,小距离内的火点定位结果失真.为了避免上述缺陷,提出了一种空间矩阵亚像素的森林小距离火点定位算法.利用卡尔曼滤波方法,对采集的森林视觉遥感图像进行滤波处理,去除图像中的干扰因素.利用物联网视觉技术,计算森林火点的空间位置,从而实现物联网视觉中的森林小距离火点定位.实验结果表明,利用算法进行森林小距离火点定位,能够避免由于遥感图像采集规范差异造成的定位特征残差扩大的缺陷,从而提高了定位的准确性.     

一种增强的基于上下文火点遥感影像识别方法

传统的火点遥感影像识别方法大多采用阈值法,但阈值的选择受区域、季节以及云天状况等多种因素的限制,因而在实际监测中往往效果不佳。针对这些问题,提出了一种增强的基于上下文信息的火点遥感影像识别方法,考虑了火点与其相邻像元之间的内在联系,在火点背景像元的确定及真实火点的判据选择等方面做了改进,在此基础上确定一组火点判据。该方法基本不受区域、时间等因素的限制,对面积较小的火点识别较为敏感,在实验中取得了较好的效果。     

AVHRR数据小火点自动识别方法的研究

利用NOAA-AVHRR数据,采用多因子分析方法,通过建立小火点自动识别模型来提取小火点燃烧信息。经实验验证,该方法能较好地减少云体、裸地对火点判断的干扰,从而在一定程度上提高了对小火点的监测精度。     

基于地球同步轨道卫星的输电线路山火监测方法研究与应用分析

为了满足输电线路山火易发地区的低漏检、高精度、大范围、高时效性火点近实时监测需求,本文以地球同步轨道卫星影像为基础,提出了一种基于MC-CNN的山火检测算法。通过结合大津算法(OTSU)和上下文算法来增加潜在火点,从而在一定程度上降低火点检测的漏检率;引入PCA算法对输入特征进行优化,构建多通道网络结构,并利用联合概率和PSO参数寻优算法获取不同通道火点识别权重,在加权平均的基础上最终判定火点;同时,采用固定高温热源和太阳耀斑对虚假火点进行去除,以降低误报率。为了验证所提算法的有效性,本文随机选取了2019年至2022年期间输电线路附近历史卫星监测山火案例,并利用已知火点样本对火点反演结果进行验证。计算结果显示,该算法的火点检测精度达到了89.4%。     

Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area,Wuda, Inner Mongolia,China

Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land‐cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach..     

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

东北地区MODIS亚像元积雪覆盖率反演及验证

以中巴资源卫星数据作为地面“真值”影像,根据东北地区地理环境与气候特点对Salomoson亚像元积雪覆盖率模型参数进行修正,反演东北地区MODIS像元积雪覆盖率,并用不同方案对模型的稳定性和精度进行分析。研究结果表明,经修正后的Salomoson亚像元积雪覆盖率反演模型对不同地貌--景观单元具有稳定性,其中较小的波动源于积雪物理性质差异、大气效应、积雪影像分类误差及影像配准误差。在东北平原区,NDSI值在0.52~0.65时,模型反演精度高,但反演雪盖率总体偏低,主要是由NDSI基于对波段反射率的非线性转换引起的;雪盖率高估的像元主要分布在城区外围以及农村居民点,而覆盖城区、乡、镇以及居民点之间道路的像元雪盖率误差小,其原因是人类活动频率影响像元内积雪组分与非积雪组分的光谱特性的差异程度。与MODIS雪产品进行对比分析,积雪覆盖率提供较传统雪盖制图更加丰富的信息,然而对林区冠层下积雪覆盖二者均未给出准确估计。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于光谱和时相特征的夏玉米遥感识别

精确提取作物种植面积一直是农业遥感关注的主要问题之一。综合运用低分辨率的时相变化特征和中分辨率的光谱特征,提出一种夏玉米识别方法。首先基于MODIS NDVI时间序列曲线,分析夏玉米在时相变化上的识别特征,构建识别模型。夏玉米纯像元利用识别模型识别,而耕地和非耕地类型的植被产生的混合像元,则基于像元分解办法获取耕地组分的NDVI时序特征,再利用识别模型判定,然后结合土地利用数据根据空间关系得到中分辨率结果;玉米与其他作物的混合像元则利用中分辨率尺度光谱差异加以区分。研究结果表明,在伊洛河流域主要农业区,识别精度达到90.33%,为作物类型识别提供了新的思路。     

CropStraw Fire Remote Sensing Monitoring and Its Algorithm Implementation

in order to obtain the information and achieve the effective control of crop straw fire spatial distribution in Central China Region.The MODIS L1B remote sensing datasets during 2014 for the main data source in this article,and combined with land use data,the farmland of Central China Region was taken as study region.Based on the enhanced contextual fire remote sensing detection algorithm,and make full use of the theoretical knowledge of quantitative remote sensing and Geospatial Data Abstraction Library (GDAL)and other technical means,to achieve the crop straw fire recognition in Central China Region.Using Ministry of Environmental Protection of the People’s Republic of China release the daily newspaper of crop straw fire in China and the standard fire products (MYD14)of MODIS for the comparative analysis of the quantitative and spatial.The results indicate that the algorithmof this paper can achieve crop straw fire remote sensing monitoring of this study region effectively,and the parameters can be adjusted in real time based on the characteristic of the study region,and improve the automation and working efficiency of crop straw fire monitoring.     

Comparison of a new algorithm with the supervised classifications

In this study, a new classification algorithm in which only the selected pixels have been attempted to be classified (selected pixels classification: SPC) has been introduced and compared with the well known supervised classification methods such as maximum likelihood, minimum distance, nearest neighbour and condensed nearest neighbour. To examine the algorithm, Landsat Thematic Mapper (TM) data have been used to classify the crop cover in the selected region. It is clearly demonstrated that the SPC method has the higher accuracy with comparable CPU times.     

Super resolution land cover mapping of hyperspectral images using the deep image prior-based approach

ABSTRACT

Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images.     

Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery

Hyperspectral determination of soil types has the potential to become an important addition to the methods used for classification and mapping of soils. In this study laboratory measured spectra of different soils, vegetation and crop residue were combined to simulate hyperspectral remote sensing imagery. The overall aim was to examine the spectral unmixing of these materials under laboratory conditions to better understand the limits to prediction of soil types and determination of cover fractions. Two different methods were utilized to mix spectra of the soil and vegetation and substantial differences were observed in the unmixing results from the different image types, particularly in mixed pixels. Results found pure soils were easily distinguished from each other when not mixed with vegetation, while some mixes of soil and vegetation were confused as pure soil spectra. The accuracy of abundance fractions retrieved in the unmixing process also varied substantially with soil type and vegetation cover.