Validating a new algorithm for estimating aerosol optical depths over land from MODIS imagery

Aerosols greatly affect the signals of satellite sensor imagery for remote sensing of land surfaces and play a dual role in global climate change and the hydrological cycle. However, there has not been a reliable method for estimating aerosol properties over land directly from multispectral remotely sensed imagery. In a recent study, a new algorithm to estimate aerosol optical depths (AODs) from Moderate‐Resolution Imaging Spectroradiometer (MODIS) imagery suitable for all land surfaces was proposed. It is based on a sequence of imagery over a period of time with the assumption that the surface property is relatively stable and atmospheric conditions vary much more dramatically. Although this algorithm was validated over several sites, more validation was necessary. In this study, this algorithm was validated using 3‐month measurements at 25 AErosol RObotic NETwork (AERONET) sites in North America. The validation results show that this algorithm can estimate AODs with close agreement with the AERONET measurements [R ² = 0.69, root mean square error (RMSE) 0.06].     

An adaptive spatially constrained fuzzy c-means algorithm for multispectral remotely sensed imagery clustering

This paper presents a novel adaptive spatially constrained fuzzy c-means (ASCFCM) algorithm for multispectral remotely sensed imagery clustering by incorporating accurate local spatial and grey-level information. In this algorithm, a novel weighted factor is introduced considering spatial distance and membership differences between the centred pixel and its neighbours simultaneously. This factor can adaptively estimate the accurate spatial constrains from neighbouring pixels. To further enhance its robustness to noise and outliers, a novel prior probability function is developed by integrating the mutual dependency information in the neighbourhood to obtain accurate spatial contextual information. The proposed algorithm is free of any experimentally adjusted parameters and totally adaptive to the local image content. Not only the neighbourhood but also the centred pixel terms of the objective function are all accurately estimated. Thus, the ASCFCM enhances the conventional fuzzy c-means (FCM) algorithm by producing homogeneous regions and reducing the edge blurring artefact simultaneously. Experimental results using a series of synthetic and real-world images show that the proposed ASCFCM outperforms the competing methodologies, and hence provides an effective unsupervised method for multispectral remotely sensed imagery clustering.     

Implementation of machine-learning classification in remote sensing: an applied review

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.     

光学遥感图像舰船目标检测与识别综述

遥感图像舰船目标自动检测与识别是遥感图像处理与分析领域备受关注的课题, 其核心任务是判断遥感图像中是否存在舰船目标,并对其进行检测、分类与精确定位, 它在海面交通监控、船只搜救、渔业管理和海域态势感知等领域具有广阔的应用前景. 本文主要围绕光学卫星遥感图像中的舰船目标自动检测与识别, 分析舰船目标检测与识别面临的难点问题, 综述当前光学遥感图像舰船检测与识别的主要处理方法, 在此基础上指出研究中尚存在的问题并展望未来的发展趋势.     

Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing

Remote sensing is a useful tool for detecting landscape fragmentation, typically by creating land-use maps from remotely sensed images acquired at different dates. Nonetheless, classification may present a number of drawbacks since it degrades the information content of images leading to the loss of continuous information about fragmentation processes. For exploratory purposes, methods to detect landscape change based on continuous information should not require any a-priori assumptions about landscape characteristics. Accordingly, Fourier transforms may represent the best algorithmic solution. In this paper, we describe a Fourier transform tool developed in a free and open-source environment to detect potential fragmentation over the landscape. We briefly introduce Fourier transforms applied to remotely sensed imagery by further showing their potential application with an empirical example. We argue that Fourier transforms represent a straightforward approach for detecting spatial fragmentation of the landscape, on the strength of their potential to detect trends in increase or decrease of complexity/heterogeneity of the landscape in an objective manner. To our knowledge, this is the first open-source tool for analysing fragmentation of the landscape in multitemporal series based on Fourier transforms, which guarantees a high robustness and reproducibility of the applied algorithms.     

TM遥感影像的地形辐射校正研究

从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言（Interactive Data Language,IDL）,结合6S大气校正模型和数字高程模型（DEM）进行编程实现。利用北京山区的TM遥感影像所做的实验表明该方法能有效地消除卫星影像中地形的影响,为影像的后续处理提供更真实的信息。     

Remote sensing in ecological botany

At the XIIth International Botanical Congress, on July 4, 1975, a new direction in scientific methodology was evaluated for the first time within the framework of an International Union of Biological Sciences—remote sensing of vegetation and the environment. Remote sensing is a method of studying the composition, structure, dynamics, and productivity of ecosystems and the state of the biosphere by means of reflectance and emittance characteristics of the earth's surface measureable from aircraft and spacecraft, and the interpretation of such remotely sensed imagery. Remote cartography is conducted with aerial and space images with a scale of from 1: 1000 to 1:30 000 000. Phytomass can be measured by comparing the dependence of the phytocenometric characteristics with the magnitude of the remotely obtained signal. Phenology and dynamics are revealed by means of optical comparison of successive images. Structural ecological investigations can be based on spatial and factoral integration of ecosystems on single, remotely-sensed images. Remote sensors record spatial and temporal variability of the reflective and emissive characteristics of vegetative ground cover. Anthropogeneous effects are recognized by indication of vegetation clearing, fires, ploughing, overgrazing, water and air pollution, and water and wind erosion.     

Street orientation detection and recognition in Landsat TM and SPOT HRV imagery

Building (street) orientation is one of the important parameters for estimation of building bulk size (height and width) from corner reflector effects using remotely sensed radar image data. However, this parameter is difficult to obtain directly from radar data. Other sensor data such as optical and near infrared data may provide possibilities. This paper reports on a method for detection and recognition of street orientation in remotely sensed Landsat TM and/or SPOT HRV imagery. The methodology includes two steps: (1) multiscale wavelet transform techniques are employed to detect edges; (2) the predominant street orientation for each 20 × 20 pixel block is then recognised by applying a simple algorithm to the detected edges which contain most of the information about street orientations.     

Mapping glacial lakes partially obscured by mountain shadows for time series and regional mapping applications

Mountain shadows in optical satellite images complicate the mapping of glacial lakes. Due to the rugged topography in periglacial alpine regions, many glacial lakes, especially smaller lakes, are partially shaded by mountain shadows in remotely sensed images. Shadows not only reduce the accuracy of lake mapping but also make changes in lake area hard to detect. In this paper, the characteristics of mountain shadows in remotely sensed imagery are explored, and their spatial relationships with regards to glacial lakes are modelled. Building on the previously developed Glacial Lakes Iterative Local Mapping (GLILM) method, a new water mapping approach is presented. The new method utilizes log-transformed spectral data and a normalized difference water index, NDWI_blue, for delineating the boundaries of lakes within shadowed regions. The application of this approach is explored within the context of mapping lakes across space and time using Landsat images in the glacially dominated Tianshan mountainous of Central Asia. The results demonstrate that glacial lakes, both in sunlit and in shaded areas, can be mapped reliably, and that the results are useful for lake change analysis studies.     

Searching satellite imagery with integrated measures

Due to the advances in imaging and storage technologies, the number and size of images continue to grow at a rapid pace. This problem is particularly acute in the case of remotely sensed imagery. The continuous stream of sensory data from satellites poses major challenges in storage and retrieval of the satellite imagery. In the mean time, the ubiquity of Internet has resulted into an ever-growing population of users searching for various forms of information. In this paper, we describe the search engine SIMR—Satellite Image Matching and Retrieval system. SIMR provides an efficient means to match remotely sensed imagery. It computes spectral and spatial attributes of the images using a hierarchical representation. A unique aspect of our approach is the coupling of second-level spatial autocorrelation with quad tree structure. The efficiency of the web-based SIMR has been evaluated using a database of images with known characteristics: cities, towns, airports, lakes, and mountains. Results show that the integrated signature can be an effective basis for accurately searching databases of satellite based imagery.     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

基于地质统计学纹理的遥感影像分类研究进展

传统的基于像素与像素基础上的遥感影像光谱分类方法忽视了邻近像素值之间潜在有用的空间信息,三十多年来,人们一直都在谋求利用遥感影像本身所固有的空间信息以加强光谱分类,尽管从事该方面研究的人一直都很少,其实现的手段主要依靠对原始影像的滤波,滤波的一般方式是生成纹理波段以指导接下来的分类。近年来,变异函数被用来表达空间依赖性,并取代简单的方差滤波成为了纹理分类的主要手段,在这篇综述性的论文中,笔者主要讨论了两类将基于地质统计学的纹理信息集成到遥感影像分类中的应用,它们代表了当前遥感影像纹理分类的主流。     

Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery

Abstract

An algorithm is developed for automatic atmospheric correction of satellite imagery of the Earth's surface. The algorithm is based solely on the satellite image being corrected and on climatology of the area. It is applicable to low resolution (1 km field of view) and high resolution (10-80m field of view) imagery of land areas for the solar spectrum. The algorithm requires that some pixels in the image will correspond to dense dark vegetation as the surface cover. Once the presence of such pixels is established, the algorithm automatically chooses these pixels, derives the atmospheric optical thickness (a measure of the amount of haze) and corrects the image. The algorithm is sensitive to the assumed reflectance of the dense dark vegetation. As a result, the accuracy of the corrected surface reflectance (p) is expected to be δp-±0.01. It is not very sensitive to the assumed aerosol characteristics, the accuracy of satellite calibration or the knowledge of the exact fraction of the image covered by the dense dark vegetation. The correction algorithm was applied to clear and hazy Landsat Multispectral Scanner images of the same area in the Washington D.C. and the Chesapeake Bay region. The aerosol optical thickness (t_a) derived from the imagery shows a good agreement with simultaneous sunphotometer measurements from the ground within δT_a=±0.20 in band 1 (0.5.0.6) and δt_a=±0.05 in band 2 (0.6-0.7μm). The images in the hazy and clear days were corrected and compared. The comparison shows, for example, that the vegetation index was corrected from 0-39 in the clear day and 0-21 in the hazy day to 0-57± 0.01 in these two days. The algorithm, in its present form, can be applied to satellite imagery that includes at least two channels in the visible part of the spectrum, preferably blue and red. Application to the Advanced Very High Resolution Radiometer type of sensor (with one broad channel in the visible part of the spectrum) would need some modifications.     

斑块状植被遥感检测研究进展

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值.传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要.利用遥感技术快速重复获取...     

Evaluation of standard and regional satellite chlorophyll-a algorithms for moderate-resolution imaging spectroradiometer (MODIS) in the Bohai and Yellow Seas,China: a comparison of chlorophyll-a magnitude and seasonality

Retrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m^?3.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.     

一种单时相高分辨率遥感影像时空融合算法

受制于传感器本身材料及卫星轨道参数,空间分辨率和时间分辨率是卫星遥感传感器固有的性能指标且难以兼备,使得高空间分辨率卫星的多时相数据合成问题至今仍是制约其广泛应用的关键问题之一。由于可有效综合空间-光谱-时间维的影像信息,多源遥感影像时空融合技术在近十年间得到迅速发展并已成为解决多时相数据合成问题的有力手段,其中基于学习的时空融合策略在合成精度上具有显著优势且应用潜力较高,但因其对字典训练过程的依赖程度较高而在融合过程中存在一定的不确定性。为提高基于学习的时空融合策略的预测精度、运算效率及鲁棒性,通过综合基于辐射归化的大气校正方法、基于误差约束的数据标准化转换机制、自适应多层递进融合策略以及高效的稀疏求解函数库,设计了一种适用于单时相高分辨率遥感影像的时空融合框架,并以国产高分二号卫星与Landsat-8卫星遥感影像为实验数据对该方法进行充分的对比性分析。实验结果表明,该融合框架不仅提升了运算效率,还在影像保真度、纹理特征描述以及光谱一致性等方面比当前的单数据对融合方法具有更好的重构质量。     

尺度不变特征转换算法的多源SAR影像匹配

鉴于直接利用SIFT算法进行SAR影响间的匹配不能得到很好的效果,考虑SIFT算法在应对噪声以及对镜像影像进行匹配的局限性,提出了针对SAR影像之间匹配的SIFT算法预处理。首先利用影像与影像之间的空间信息进行匹配,之后利用SIFT算法进行局部特征点匹配,通过采用RANSAC进行错配点的去除,实现SAR影像的高精度配准。实验结果表明,该文提出的预处理以及错配点的去除给利用SIFT算法进行SAR影像的匹配提供了可能。     

Adaptive thresholding of texture towards white region normalization

Human colour vision has the ability to recover a measure of contextdependent surface reflectance from observed areas. This can be seen as an analogue to radiometric corrections employed in remote sensing. Procedures based on human colour vision applied in the context of remote sensing could simplify image preprocessing and classification. This study evaluates an algorithm based on Land's colour vision or 'retinex' theory. When tested on remotely sensed images, the algorithm had difficulty coping with the presence of image texture. A new framework is presented that adapts Land's procedure to processing of remotely sensed images using adaptive thresholding techniques.     

Technical note On estimating measurement error in remotely-sensed images with the variogram

Previously, several methods have been developed to estimate the signal-to-noise ratio of remotely sensed imagery. Of these, the most appropriate is a method based on spatial dependence for estimating the signal-to-noise ratio of Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery. The intercept on the ordinate of the modelled sample variogram, known as the nugget variance, is used to estimate noise. However, while the nugget variance is due to measurement error, it depends also on short-range spatial variation that has not been measured, underlying variation that has been measured (but which may result in a non-linear form of variogram near the ordinate), sampling effects, and the choice of model fitted to the sample variogram. For remotely-sensed imagery there is no short-range variation that has not been measured because the pixels are contiguous or overlapping. Further, there are usually many pixels and so sampling effects are negligible. However, it is impossible to account for the form of the variogram near the ordinate when selecting a mathematical model. Consequently, while the nugget variance remains as the most appropriate method of estimating measurement error in remotely sensed images, it may be less reliable than previously thought.     

Hyperspectral change detection: an experimental comparative study

ABSTRACT

The Earth’s surface is constantly changing due to variations originating from the increasing human population. In the last decade, numerous methods were presented in the literature for change detection using multispectral image data. Owing to the increasing availability of hyperspectral images, these methods are now being applied to hyperspectral images. The main objective of this study is to present different change detection methods in hyperspectral imagery. Numerous algorithms (more than 43 algorithms) have been proposed for change detection in hyperspectral imagery over the last decade. In this work, we provide a comparative review of these algorithms through experimental results. We place the algorithms in five major groups: (1) match-based, (2) transformation-based, (3) direct classification-based, (4) post-classification-based, and (5) hybrid-based. We evaluate and compare the performances of all five groups using two real-world data sets of multi-temporal hyperspectral imagery. This comparative study investigates the advantages and disadvantages of the effects of preprocessing steps in the efficiency of the hyperspectral change detection (HSCD) methods. These preprocessing steps are considered in four scenarios, including: (1) considering only spatial or geometric correction without noise reduction and spectral correction; (2) spatial, atmospheric, and radiometric corrections without noise reduction; (3) spatial correction and noise reduction without atmospheric and radiometric corrections; and (4) spatial, atmospheric, and radiometric correction with noise reduction. The empirical results, followed by a summary of the pros and cons of each algorithm, aim to help researchers select the procedures with the best characteristics for HSCD applications.