无人机自主着陆图像识别压缩技术研究

近些年来,随着遥感测绘需求日益增加,测绘型旋翼无人机的使用变得愈加频繁。测绘型旋翼无人机以其独特的气动性能及应用特点,使得其与固定翼无人机可以共同应用于航片拍摄。目前,由于地形及地域等多重因素的影响,测绘型旋翼无人机在执行任务期间经常遇到气流扰动、遥控信号干扰、GPS辅助信号中断等意外情况,需要应用其自主模式进行辅助着陆。而要实现自主着陆首先需要解决的是无人机对地面着陆地标的识别处理等一系列问题,本文就相关的图像识别压缩技术进行设计研究。     

基于GF-1的坑塘信息精确提取方法研究

以国产高分一号2 m全色、8 m多光谱遥感影像作为数据源,选择廊坊市文安县作为研究区域,针对坑塘信息精确提取问题,采用单波段阈值法、监督分类法、面向对象方法等进行实验研究,重点研究了面向对象方法,结合运用遥感影像光谱、纹理、形状等特征,分析坑塘与其他地物之间的差异,最终实现坑塘信息的精确提取。     

基于GIS的榆林市乡镇地名分布特征研究

利用不同的网络公开地名数据源进行数据融合,获得榆林市乡镇级居民地地名数据,在此基础上,对地名数据进行统计分类,引入地名密度和地名点距离的指标,运用GIS的分析方法,对榆林市乡镇级地名数据进行景观分析。研究得出:榆林乡镇地名从东南向西北部递减,并且呈现出聚集的分布特征;榆林市的自然人文特征直接体现在其主要地名分类的分布上,山水类地名的分布反映了榆林西北部沙漠干旱,东南部黄土丘陵、河流较密的自然特点,姓氏、军事类地名的分布则反映了榆林市的移民文化、多民族混居的民族特色,以及古代军事价值高的特点。     

辽宁省低丘缓坡调查技术探讨

作为一项全新的土地管理和资源利用工作,低丘缓坡调查目前正在全国积极开展。本文结合辽宁省低丘缓坡调查项目,简要介绍了低丘缓坡调查技术流程,详细阐述了调查方法和手段,为其他各省市的低丘缓坡调查工作提供了有益的参考。     

An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods.     

Detecting shadows in multi-temporal aerial imagery to support near-real-time change detection

Multi-temporal aerial imagery captured via an approach called repeat station imaging (RSI) facilitates post-hazard assessment of damage to infrastructure. Spectral-radiometric (SR) variations caused by differences in shadowing may inhibit successful change detection based on image differencing. This study evaluates a novel approach to shadow classification based on bi-temporal imagery, which exploits SR change signatures associated with transient shadows. Changes in intensity (brightness from red–green–blue images) and intensity-normalized blue waveband values provide a basis for classifying transient shadows across a range of material types with unique reflectance properties, using thresholds that proved versatile for very different scenes. We derive classification thresholds for persistent shadows based on hue to intensity ratio (H/I) images, by exploiting statistics obtained from transient shadow areas. We assess shadow classification accuracy based on this procedure, and compare it to the more conventional approach of thresholding individual H/I images based on frequency distributions. Our efficient and semi-automated shadow classification procedure shows improved mean accuracy (93.3%) and versatility with different image sets over the conventional approach (84.7%). For proof-of-concept, we demonstrate that overlaying bi-temporal imagery also facilitates normalization of intensity values in transient shadow areas, as part of an integrated procedure to support near-real-time change detection.     

唐山市区土地利用的Landsat 8影像分层分类

针对土地利用遥感分类方法多样、分类精度高低不一等问题,该文以土地利用变化明显的唐山市路南区、路北区为研究区域,并以中分遥感影像Landsat 8OLI为信息源,在对地类样本进行可分离性分析的基础上,建立研究区土地利用分层分类体系。通过监督分类实验,选择分类效果最好、分类精度最高的最大似然分类器进行地类初分;通过绘制归一化植被指数(NDVI)、归一化建筑指数(NDBI)、两指数差值(NDVI-NDBI)的曲线及地类光谱特征曲线,建立决策树分类规则,进行地类再分。该方法可以较好地完成多种土地利用二级地类的划分,有助于提高中分影像土地利用分类效率。     

地形图要素辅助下的影像面状地物提取

针对遥感影像与地形图变化检测中影像面状地物的提取问题,该文提出了一种地形图要素辅助下的影像面状地物提取方法。该方法首先利用配准后落在影像面状地物内的地形图面状要素中心进行区域生长,然后以人工选取的各类地物的初始样本和上述区域生长出影像上的各类地物作为训练样区进行监督分类,最终实现影像面状地物提取。实验结果表明,该方法能够有效地提取影像面状地物,并且自动化程度较高。     

Segmentation and classification of high spatial resolution images based on Hölder exponents and variance

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%.