一种小波变换融合的改进算法及其效果评价

在基于传统小波变换的遥感影像融合中,仅用到低分辨率影像经小波分解后的低频信息和高分辨率影像经小波分解后的高频信息,简单地对高分辨率影像的低频信息进行舍弃。文章提出一种对高分辨率影像的低频信息也加以充分利用的小波变换融合规则改进算法,并应用于SPOT5遥感影像的融合。通过以客观指标为标准进行效果评价表明,改进算法后,融合影像不仅清晰度提高,而且能最大程度地保留原多光谱影像的光谱信息。     

多特征融合的遥感图像分类

针对高分辨率遥感图像特点,提出了一种多特征融合的分类方法。该方法首先改进了原始的视觉词袋生成算法;然后,分别提取图像的视觉词袋局部特征、颜色直方图特征以及Gabor纹理特征;最后采用支持向量机进行分类,并对多特征分类结果进行自适应综合。采用一个具有2 100幅图像的大型遥感图像分类公共测试数据集进行分类实验,与仅用单一特征分类方法的最高分类精度相比,本文多特征融合的遥感影像分类方法总体平均分类精度提高了10%,表明本文提出方法是一种有效的高分辨率遥感图像分类方法     

一种改进的高分辨率遥感影像分割方法及应用

图像分割是进行遥感信息监测的重要前提条件和基础,文章阐述了对遥感影像进行分割的必要条件,介绍了对遥感影像进行分割的基本原理,并且介绍了几种图像分割的方法。根据影像分割在城市房地产监测中的应用,文章提出了一种基于改进的H指标分割方法,并通过实际试验确定了利用此方法对高分辨率遥感影像进行分割的指标。     

高分辨率遥感影像中提取道路网方法综述

高分辨率遥感影像中道路网的提取是智能地物提取和分析的重要方面。针对其特点,介绍了高分辨率遥感影像上道路网提取的基本思想和步骤,从提取要素层次的角度对现有的道路网提取方法进行了分析和综述,并指出当前高分辨率遥感影像道路网提取方法需要进一步解决的遮挡、地物特征类似、地物复杂等问题。展望了未来利用高层次知识、图像融合技术、三维信息等高效提取道路网的可行性。     

遥感应用分析中影像分割方法

影像分割是数字图像模式识别的基础, 是实现从影像上进行目标计算机自动提取的第一步工作, 在遥感应用分析中具有重要意义。近年来, 随着大量的高质量的高分辨率遥感影像出现, 其相关应用迫切需要有效的、考虑其高分辨率影像特点的影像分割方法。但由于高分辨率遥感影像本身的一些特点, 遥感应用分析中传统常用的分割方法难以达到满意的效果。对遥感应用分析中的影像分割方法进行了总结和分析, 并结合高分辨率遥感影像的特点, 分析了高分辨率遥感影像分割的技术难点, 说明了基于像素的传统影像分割方法的局限性, 着重阐述了数学形态学、面向对象分析等方法能更充分地利用影像中目标的色调、纹理、几何等属性, 因此在高分辨率遥感影像分割方面更具有优势。最后, 探讨了遥感应用分析中影像分割方法的发展趋势。     

基于深度学习与超分辨率重建的遥感高时空融合方法

针对遥感影像的“时空矛盾”,提出一种改进STARFM的遥感高时空融合方法。利用SRCNN对低分辨率影像进行超分辨率重建,由于所融合的2组影像分辨率差距过大,网络训练困难,先将2组影像均采样至某一中间分辨率,使用高分辨率影像作为低分辨率影像的先验知识进行SRCNN重建,再将得到的中间分辨率影像重采样后以原始高分辨率影像作为先验知识进行第2次SRCNN重建,得到的最终重建图像相比原先使用插值法重采样所得图像,在PSNR和SSIM上均有提升,缓解了传感器差异所造成的系统误差。STARFM融合方法在筛选相似像元与计算权重时均使用专家知识提取人工特征,基于STARFM时空融合的基本思想,以SRCNN作为基本框架 自动提取特征,实验结果表明,其MSE值相比原方法更低,进一步提高了遥感时空融合的质量,有利于充分利用遥感影像。     

高分辨率多光谱遥感影像中城区道路信息的自动提取

提出一种从高分辨率遥感影像提取城市区域道路网络的方法。该方法采用改进的数学形态学运算方法对影像进行分割,进而得到粗略道路信息网,然后利用道路网的几何特征实现道路与建筑物的有效区分,最后通过抽骨架的方法获得最终道路网中心线。试验数据为某一城区高分辨率卫星影像,并对最终提取的结果进行了评价,结果表明,所提出的方法能够从高分辨率多波段卫星遥感影像上精确、有效、自动提取城区道路网络。     

基于遥感时空融合的升金湖湿地生态水文结构分析

利用遥感技术开展湖泊湿地生态水文结构分析对维持其生态服务功能具有重要意义,但受大气状况的影响会造成特定水位下可用高分辨率遥感影像的缺失,而遥感时空融合技术为弥补这一缺陷提供了重要途径。以安徽省升金湖湿地为研究区,根据改进后的时空自适应反射率融合模型（ESTARFM）模拟生成高时空分辨率遥感影像,评价模拟遥感影像的数值精度,进而分析了升金湖湿地的生态水文结构。结果表明： ① ESTARFM模型能够有效模拟不同水位下湖泊湿地的高分辨率遥感影像,融合影像与真实影像在近红外波段、短波红外波段反射率的相关系数分别达到0.93和0.91,且输入数据与融合数据的日期间隔越短,模拟精度越高;② 基于不同水体指数的水体提取效果表明,新型组合水体指数（NCWI）更适用于湖泊湿地的水体信息提取;③ 对升金湖湿地生态水文结构分析可知,湿地中心区、适宜活动区和非适宜区分别约占该湿地总面积的32.8%、12.1%和55.1%。     

基于ARSIS概念的遥感影像小波包融合

研究了基于ARSIS概念的遥感影像的小波包融合方法。利用小波包分解高分辨率影像和多光谱影像,在融合影像的低频小波包系数重构时采用波段间交互构建模式,即包含了多光谱影像低频小波包系数,也包含了高分辨率影像低频小波包系数。融合影像的高频小波包系数以局部方差为规则进行择优选取。融合实验结果证明,新方法在对原多光谱影像信息的保持上优于IHS方法,同时在保持高分辨率影像的细节信息上有所改善。     

高空间分辨率遥感图像分类的SSMC方法

近年来随着高空间分辨率遥感的发展。影像的空间细节描述能力得到提高，像元之间的空间相关性得到增强，使传统的遥感影像光谱分类方法面临着巨大的挑战。基于此背景，提出了高分辨率遥感影像分类的SSMC（spatial and spectral mixed classifier）方法，旨在同时采用光谱和空间特征进行遥感影像分类。本文是基于SSMC方法的一个具体的实验，通过多尺度的空间金字塔构造每个像元的空间参数，整合影像的光谱信号和空间信息进行高分辨率遥感影像分类。实验结果证明，SSMC方法对于提高高分辨率遥感影像的分类精度具有积极的意义。     

High-resolution satellite image fusion using regression kriging

Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for applications especially for same-sensor and single-date images. However, colour distortion is a common problem for multi-sensor or multi-date image fusion. In this study, a new image fusion method of regression kriging is presented. Regression kriging takes consideration of correlation between response variable (i.e., the image to be fused) and predictor variables (i.e., the image with finer spatial resolutions), spatial autocorrelation among pixels in the predictor images, and the unbiased estimation with minimized variance. Regression kriging is applied to fuse multi-temporal (e.g., Ikonos, QuickBird, and OrbView-3) images. The significant properties of image fusion using regression kriging are spectral preservation and relatively simple procedures. The qualitative assessments indicate that there is no apparent colour distortion in the fused images that coincides with the quantitative checks, which show that the fused images are highly correlated with the initial data and the per-pixel differences are too small to be considered as significant errors. Besides a basic comparison of image fusion between a wavelet based approach and regression kriging, general comparisons with other published fusion algorithms indicate that regression kriging is comparable with other sophisticated techniques for multi-sensor and multi-date image fusion.     

Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime, which however, raises new challenges such as severe color distortion, more complex lighting conditions, and lower contrast. Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime, we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm. We first propose a human visual system (HVS) inspired color correction model, which is effective for removing the color deviation on nighttime hazy images. Then, we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion, where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids. Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast, color fidelity, and visibility. In addition, our method outperforms the state-of-the-art methods qualitatively and quantitatively.     

基于融合策略的改进Retinex低照度图像增强算法

针对Retinex算法处理低照度彩色图像出现色彩失真,边缘保持性差等问题,提出一种基于融合策略的改进Retinex低照度图像增强算法;该算法首先在YIQ颜色空间提取亮度分量Y,对其进行MSR算法增强;然后采用高斯-拉普拉斯算子对彩色图像的RGB三个分量进行边缘检测,将其叠加合成后转换成灰度图;最后使用小波变换将两幅图像融合得到新的亮度分量,将其与I、Q分量融合后转回RGB颜色空间,从而获得色彩保真度高、细节清晰的图像;实验结果表明,该方法有效提高了图像边缘细节信息,避免了色彩失真,具有很好的视觉效果。     

De-hazing and enhancement method for underwater and low-light images

Because underwater and low-light images have different characteristics, there are few methods to jointly improve the visibility of these images. This paper proposes a de-hazing and enhancement method for underwater and low-light images to describe the two types of images uniformly. Multi-scale retinex color recovery (MSRCR) and guided filtering methods are used for de-hazing; the proposed method of white balance fusion global guided image filtering (G-GIF), effectively solve the problems of dim light, color distortion, and loss of edge details. Experiments show that compared with other methods, this method can effectively solve the image exposure, and at the same time, it can better protect and enhance the image’s color saturation and edge texture details, thus achieving a very good visual effect.

     

变压器油下图像增强算法

为解决变压器检测机器人在变质、变色的变压器油内部采集的图像存在色彩失真、对比度低等问题,提出一种变压器油下图像融合增强算法.首先,利用完美反射算法对图像进行白平衡处理,以消除油下光照强度不均匀对图像颜色的影响,使得色彩更加均衡;然后,对色彩校正的图像进行自适应伽马校正,以提高图像的对比度;最后,采用多尺度融合策略将色彩校正后的图像与自适应伽马校正处理后的图像进行融合,得到变压器油下清晰的图像.实验结果表明,经所提出算法处理后的变压器油下图像色彩鲜明、细节丰富,与原始图像相比,图像质量评价指标(UCIQE)、特征点匹配个数以及信息熵均有显著提高,能够为变压器内部故障检测提供清晰的数据.     

基于内容特征和风格特征融合的单幅图像去雾网络

基于深度学习的方法在去雾领域已经取得了很大进展,但仍然存在去雾不彻底和颜色失真等问题.针对这些问题,本文提出一种基于内容特征和风格特征相融合的单幅图像去雾网络.所提网络包括特征提取、特征融合和图像复原三个子网络,其中特征提取网络包括内容特征提取模块和风格特征提取模块,分别用于学习图像内容和图像风格以实现去雾的同时可较好地保持原始图像的色彩特征.在特征融合子网络中,引入注意力机制对内容特征提取模块输出的特征图进行通道加权实现对图像主要特征的学习,并将加权后的内容特征图与风格特征图通过卷积操作相融合.最后,图像复原模块对融合后的特征图进行非线性映射得到去雾图像.与已有方法相比,所提网络对合成图像和真实图像均可取得理想的去雾结果,同时可有效避免去雾后的颜色失真问题.     

Research on ALI Image Fusion Algorithm based on Improved Brovey Transform

Aiming at the problems of spectral information loss and spectral distortion in traditional Brovey Transform (BT) fusion, the adaptive weighted average is introduced to improve it. Taking EO-1 ALI multispectral imagery as an example, a new multispectral image fusion algorithm based on improved BT is proposed. Information entropy, average gradient, correlation coefficient and root mean square error are used to comprehensively evaluate and compare the fusion effects of this algorithm, so as to verify the effectiveness and superiority of the multispectral image fusion algorithm based on improved BT. Experimental results show the multispectral fusion image using this improved algorithm has better spectral information and spatial resolution, and its visual effects and spatial texture features have been significantly improved, and the color information of the source image has been well extended, and the brightness is relatively moderate; this improved algorithm can reduce the loss and distortion of spectral information in the fusion process, and has obvious advantages in maintaining spectral information and clarity compared with the traditional BT fusion method.     

基于改进Brovey变换的ALI影像融合算法研究

针对传统Brovey变换（Brovey Transform,BT）融合存在的光谱信息丢失及光谱扭曲等问题,引入自适应加权平均对其进行改进,并以EO-1 ALI多光谱影像为例,提出一种新的基于改进BT的多光谱影像融合算法。并分别选用信息熵、平均梯度、相关系数、均方根误差等参数对影像融合效果进行综合评价与对比分析,从而验证该算法的有效性和优越性。结果显示：采用改进后的BT融合算法的多光谱融合影像具有较好的光谱信息和空间分辨率,其视觉效果和空间纹理特征都有显著改善,并且较好地延续了源影像的色彩信息,亮度相对适中;能减少融合过程中光谱信息的损失和畸变,在保持光谱信息和清晰度方面与传统BT融合算法相比具有明显优势。     

Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Underwater imagery suffers from severe effects due to selective attenuation and scattering effects when light travels through water medium. Such damages limit the ability of vision tasks and reduce image quality. There are a lot of enhancement methods to improve the quality of underwater image. However, most of the methods produce distortion effects in the output images. The proposed natural-based underwater image color enhancement (NUCE) method consists of four steps. The first step introduces a new approach to neutralize underwater color cast. The inferior color channels are enhanced based on gain factors, which are calculated by considering the differences between the superior and inferior color channels. In the second step, the dual-intensity images fusion based on average of mean and median values is proposed to produce lower-stretched and upper-stretched histograms. The composition between these histograms improves the image contrast significantly. Next, the swarm-intelligence based mean equalization is proposed to improve the naturalness of the output image. Through the fusion of swarm intelligence algorithm, the mean values of inferior color channels are adjusted to be closed to the mean value of superior color channel. Lastly, the unsharp masking technique is applied to sharpen the overall image. Experiments on underwater images that are captured under various conditions indicate that the proposed NUCE method produces better output image quality, while significantly overcoming other state-of-the-art methods.