An approach based on discrete wavelet transform to unsupervised change detection in multispectral images

Change vector analysis (CVA) and Spectral Angle Mapper (SAM) are widely used for change detection in multitemporal multispectral images. CVA and SAM describe the difference from the perspective of vector magnitude and spectral angle, respectively. It has been proved that three change categories may occur in a changed pixel; however, CVA or SAM alone can only detect two of the three change categories properly. Hence, we propose a novel approach integrating the advantages of them to acquire a better change map. This approach, based on discrete wavelet transform (ABDWT, i.e. approach based on discrete wavelet transform), obtains two difference images by using CVA and SAM, and then yields a novel difference image by fusing them in the coefficients domains of discrete wavelet transform. Experimental results from a simulated and two real data sets validate the effectiveness of the proposed approach. In the first real data set, the proposed approach can identify 14,916 changed pixels while the best result of other methods is 14,806. In the second real data set, the proposed approach detects 3203 changed pixels, while the maximum of other methods is 3189.     

Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis

With the increasing accessibility of high or very high spatial resolution remote sensing images, more and more change detection works are conducted on the unit of image object, i.e. the object-based change detection (OBCD). Change vector analysis (CVA) is a promising tool for unsupervised OBCD because it can provide reasonable interpretation of the change and an insight into the type of change. However, various features and attributes of image object produce complex high dimensional feature space that poses new challenges to measure the overall change magnitude and direction in CVA. This paper presents a new approach to measure the overall change magnitude of an image object by integrating its spectral and textural attributes, self-adaptively. The new approach was compared with the standard CVA magnitude, Mahalanobis distance, and a recent Self-Adaptive Weight CVA (SAW-CVA) magnitude for unsupervised OBCD with the same OTSU auto-thresholding method. Two cases were investigated where Case I used bi-temporal WorldView multispectral images (4-band) and Case II employed bi-temporal three-band images obtained from Google Earth. Results in the two cases both demonstrated that the new approach outperforms the others for unsupervised OBCD. The percentage correct classification of the new approach, SAW-CVA, standard CVA, and Mahalanobis are 80.28%, 77.93%, 72.77%, and 61.50% in Case I, and 91.80%, 90.16%, 88.52%, and 90.16% in Case II. Further analysis indicated that the new approach gives more reasonable self-adaptive weights and it does not require the empirical self-adaptive index as compared with the recent SAW-CVA method.     

Change detection in coral reef communities using Ikonos satellite sensor imagery and historic aerial photographs

For decades, aerial photographs have been the only source of very high spatial resolution data for coral reef researchers. With the launch of the Ikonos satellite in 1999, imagery with a 4 m spatial resolution in multispectral mode can now be combined with historical aerial photographs for change detection. We demonstrate this potential by combining two aerial photographs (1981 and 1992) and an Ikonos image (2000) to detect change in the coral reef communities for Carysfort Reef, Florida, USA. The results show a loss of 'coral-dominated' bottom from 52% (1981) to 16% (1992) to finally 6% (2000), a trend similar to in situ observations.     

Classification automatique assistée par une analyse de texture,des paysages de la Pointe d'Arçay d'après des données Thematic Mapper

Abstract

Cet article présente une classification automatique des unités de paysages de la Pointe d'Arçay d'aprés des données Thematic Mapper (TM), avec un algorithme de classification non supervisée d'agrégation autour de centres mobiles, en utilisant non seulement des informations radiométriques du TM mais aussi un certain nombre de caractéristiques texturales des paysages. La qualité de la classification multispectrale est améliorée par l'introduction d'une analyse de texture

An approach to automatic classification of landscape using LANDSAT-5 Thematic Mapper (TM) data is presented with an example of application for the Arçay spit (Véndee, France). First of all, using unsupervised classification of aggregation round mobile centres (the dynamic-cluster algorithm), a multispectral classification is realized withTM bands 1,2,3,4, 5 and 7, and then a texture analysis is introduced to eliminate classification confusion between certain classes. The classification quality is improved owing to the texture analysis.     

Un nouvel algorithme de classification non dirigée

Abstract

Un nouvel algorithme est proposé pour une classification non dirigée des données multispectrales de. télédétection L'utilisateur indique seulement le nombre de classes à déterminer sans l'introduction nécessaire de leurs centres initiaux. L'a|gorithme créera automatiquement des centres de classe selon la structure des données. La performance de la méthode proposée a été évaluée à travers un exemple de classification multispectrale non dirigée des données Thematic Mapper.

This Letter presents a new algorithm for the unsupervised classification of multispectral remotely-sensed image data. A user inputs the desired number of classes without the introduction of their initial class centres. The algorithm automatically creates the class centres according to the separability of data. The performance of the approach has been evaluated through an example of unsupervised classification of Thematic Mapper multispectral image data.     

The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images

Intensity hue saturation (IHS) and wavelet decomposition are two distinct fusion methods used for enhancing the spatial resolution of multispectral images by exploiting a high-resolution panchromatic image. In this paper, a combination of the IHS transform and redundant wavelet decomposition is proposed as a general method for fusing multisensor images. The principle consists of transforming low-resolution multispectral images into IHS independent components. The low-resolution intensity component is fused with the high-resolution panchromatic image in the redundant wavelet domain through an appropriate model. Subsequently, the high-resolution intensity produced is substituted to the low-resolution intensity. High spatial resolution multispectral images are then obtained through an inverse IHS transformation. SPOT images are used to illustrate the superiority of this approach over the IHS fuser in terms of preservation of spectral properties.     

Change detection techniques

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

Abbreviations used in this paper

6S second simulation of the satellite signal in the solar spectrum

ANN artificial neural networks

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

AVHRR Advanced Very High Resolution Radiometer

AVIRIS Airborne Visible/Infrared Imaging Spectrometer

CVA change vector analysis

EM expectation–maximization algorithm

ERS-1 Earth Resource Satellite-1

ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image

GIS Geographical Information System

GS Gramm–Schmidt transformation

J-M distance Jeffries–Matusita distance

KT Kauth–Thomas transformation or tasselled cap transformation

LSMA linear spectral mixture analysis

LULC land use and land cover

MODIS Moderate Resolution Imaging Spectroradiometer

MSAVI Modified Soil Adjusted Vegetation Index

MSS Landsat Multi-Spectral Scanner image

NDMI Normalized Difference Moisture Index

NDVI Normalized Difference Vegetation Index

NOAA National Oceanic and Atmospheric Administration

PCA principal component analysis

RGB red, green and blue colour composite

RTB ratio of tree biomass to total aboveground biomass

SAR synthetic aperture radar

SAVI Soil Adjusted Vegetation Index

SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image

TM Thematic Mapper

VI Vegetation Index     

Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes

Crisp and fuzzy competitive learning network schemes have been designed for classification of multispectral IRS-1B satellite images. For supervised learning, an extension of competitive learning network with a Grossberg layer, sometimes known as a 'forward only' Counter-propagation Network (CPN) has been used. The 'concept of winner' of a classical Kohonen's network has been fuzzified in this model. This model is found to yield much better accuracy than the crisp Kohonen's network and marginally better accuracy than the Maximum Likelihood Classifier. The results are discussed.     

Testing two methods for mapping water hyacinth (Eichhornia crassipes) in the Greater Letaba river system,South Africa: discrimination and mapping potential of the polar-orbiting Sentinel-2 MSI and Landsat 8 OLI sensors

ABSTRACT

Early detection and mapping of the spatio-temporal distribution of invasive water hyacinth (Eichhornia crassipes) in inland hydrological systems are vital for a number of water resource management-related reasons. Field surveys and current climate change projections (associated with longer dry spells, and shortened rain seasons) have shown that climate change and the rapid spread of aquatic invasive species are increasingly affecting inland surface water availability in semi-arid regions of Southern Africa. It is upon this premise that accurate, reliable, and timely information on the spatio-temporal distribution and configuration of water hyacinth is required in tracing their evolution and propagation in affected areas as well as in potential vulnerable areas. This work, therefore, attempts to test two robust push-broom multispectral sensors: Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) in identifying, detecting, and mapping the spatial distribution and configuration of invasive water hyacinth in a river system. The results of the study show that water hyacinth in small reservoirs can be mapped with an overall accuracy of 68.44% and 77.56% using Landsat 8 and Sentinel-2 data, respectively. The results further demonstrated the blue, red, red edge (RE) 1, short-wavelength infrared (SWIR)-1, and SWIR-2 of both satellite data sets as the critical and outstanding spectral regions in detecting and mapping water hyacinth from other land-cover types. Overall, the study highlights the unexploited prospects of the new noncommercial multispectral sensors in monitoring invasive species infestation from inland surface waterbodies in semi-arid regions (i.e. smaller reservoirs).     

TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm

Image fusion represents an important tool for remote sensing data elaborations. This technique is used for many purposes. Very often it is used to produce improved spatial resolution. The most common situation is represented by a pair of images: the first acquired by a multispectral sensor with a pixel size greater than the pixel size of the second image given by a panchromatic sensor (PAN). Starting from these images fusion produces a new multispectral image with a spatial resolution equal, or close, to that of the PAN. Very often fusion introduces important distortions on the pixel spectra. This fact could compromise the extraction of information from the image, especially when using an automatic algorithm based on spectral signature such as in the case of image classification. In this work we present the analysis of two fusion methods based on multiresolution decomposition obtained using the 'a tròus' algorithm and applied to a pair of images acquired by Thematic Mapper (TM) and Indian Remote Sensing (IRS)-1C-PAN sensors. The methods studied are also compared with two classical fusion methods, the intensity, hue and saturation (IHS) and standardized principal components (SPC). Fused results are studied and compared using various tests including supervised classification. Most of the tests used have been extracted from literature regarding the assessment of spatial and spectral quality of fused images. This study shows that the methods based on multiresolution decomposition outperform the classical fusion methods considered with respect to spectral content preservation. Moreover, it is shown that some of the quality tests are more significant than others. The discussion of this last aspect furnishes important indications for data quality assessment methods.     

自适应权重注入机制遥感图像融合

目的 遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。方法 使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。结果 在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC（correlation coefficient）和光谱角映射SAM（spectral angle mapper）评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS（erreur relative globale adimensionnelle de synthèse）、峰值信噪比PSNR（peak signal-to-noise ratio）、相对平均光谱误差RASE（relative average spectral error）、均方根误差RMSE（root mean squared error）、光谱信息散度SID（spectral information divergence）等评价指标上均为最优值。结论 本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。     

Processing airborne MSS data for vegetation studies

Abstract

This letter discusses the need for accurate processing of airborne multispectral data for vegetation studies. The processing includes radiometric and atmospheric corrections and allowances for environmental influences on the data. A methodology for such corrections using easily available collateral data is described and successfully applied to airborne multispectral data recorded in the Bawtry area of South Yorkshire, England.     

Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion

ABSTRACT

Hyperspectral remote sensing plays an important role in a wide variety of fields. However, its specific application for land surface analysis has been constrained due to the different shapes of thick, opaque cloud cover. The reconstruction of missing information obscured by clouds in remote-sensing images is an area of active research. However, most of the available cloud-removal methods are not suitable for hyperspectral images, because they lose the spectral information which is very important for hyperspectral analysis. In this article, we developed a new spectral resolution enhancement method for cloud removal (SREM-CR) from hyperspectral images, with the help of an auxiliary cloud-free multispectral image acquired at different times. In the fixed hyperspectral image, spectra of the cloud cover pixels are reconstructed depending on the relationship between the original hyperspectral and multispectral images. The final resulting image has the same spectral resolution as the original hyperspectral image but without clouds. This approach was tested on two experiments, in which the results were compared by visual interpretation and statistical indices. Our method demonstrated good performance.     

Detection of surface soil variation using high-resolution satellite data: results from the U.K. SPOT-simulation investigation

Abstract

A problem in using multispectral scanner(MSS) data for soil and landsystem analysis in north-west Europe is the poor spatial resolution which is insufficient to provide adequate within-field data. The SPOT satellite system will provide MSS data at 20m resolution and panchromatic data at 10m resolution. For any given ground feature the SPOT MSS mode will provide considerably more sample areas than LANDSAT 80m data. The object of this study is to determine how far variation in surface soil parameters can be detected and quantified on the basis of SPOT data.     

Unsupervised classification of multispectral Landsat images with multidimensional particle swarm optimization

This article proposes a novel unsupervised classification approach for automatic analysis of multispectral Landsat images. The automatic classification of the information in multidimensional (MD) Landsat data space by dynamic clustering is addressed as an optimization problem and two recently proposed heuristic techniques based on Particle Swarm Optimization (PSO) are applied to determine the optimal (number of) clusters in a given input data space: distance metric and a proper validity index function. The first technique, the so-called MD-PSO, re-forms the native structure of swarm particles (agents) in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Fractional global best formation (FGBF) basically collects all promising dimensional components and fractionally creates an artificial global best (aGB) agent that has the potential to be a better ‘guide’ than the swarm’s native global best position (gbest) agent. In this study, the proposed dynamic clustering approach based on MD-PSO and FGBF techniques is applied to automatically classify the colour-coded representations of the multispectral (MD) Landsat data. The approach has been applied to real-world multispectral data and it provided quite encouraging results compared to the traditional K-means and ISODATA (iterative self-organizing data analysis) clustering methods. The proposed unsupervised technique determines the true number of classes within Landsat data for optimal classification performance while preserving spatial resolution and textural information in the classification map.     

改进NSCT和IHS变换相结合的遥感影像融合

目的 目的为了增强多光谱和全色影像融合质量,提出基于脉冲耦合神经网络（PCNN）的非下采样Contoulet变换（NSCT）和IHS变换相结合的融合方法。方法 先对多光谱图像进行IHS变换提取亮度I分量,采用主成分分析增强I分量得到新的I⁺分量;然后通过NSCT变换分别对I⁺分量和全色图像进行分解,并采用边缘梯度信息激励的PCNN得到融合图像的低频和高频分量;最后进行NSCT逆变换、IHS逆变换得到融合图像。结果 利用资源一号02C卫星数据进行实验,结果表明该算法在保留光谱信息的同时提高了图像空间分辨率,获得了较好的融合效果。结论 结合NSCT和IHS变换的融合方法在视觉效果和客观评价指标上都优于常用的图像融合方法。     

Lithological discrimination in central Snowdonia using airborne multispectral scanner imagery†

Abstract

An evaluation has been carried out of the use of 11-channel airborne multispectral scanner data as an input to large-scale geological mapping in upland Britain. The optimum index factor (OIF) provides a ranking of possible three-band combinations in terms of their uncorrelated spectral data content, while principal components analysis may be used to compress multi-channel data, thereby minimizing information loss. An assessment of the geological information content of various imagery products concluded that both high-value OIF and principal component images were useful for lithological discrimination of solid and superficial deposits whereas aerial photographs were better for identifying structural features.     

低成本便携式多光谱成像系统的研发及优化

目的 针对现有多光谱成像系统存在成本高、结构复杂、操作难度大和响应速度慢等问题。因此，本文提出了一种基于脉冲调制的低成本便携式多光谱成像系统，并采用客观图像质量评估（image quality assessment，IQA）的方法对其系统参数进行优化。方法 该系统主要由光源模块、控制模块、图像采集模块和图像分析模块4部分组成。光源模块采用9个波长的LED （light emitting diode）阵列，其中心波长为365 nm、390 nm、460 nm、515 nm、585 nm、620 nm、650 nm、730 nm和840 nm；控制模块主要包括LED驱动电路和USB （universal serial bus）电源，可以通过发送一定时间间隔的脉冲波来分时点亮LED，并通过一定阻抗匹配使LED发光强度达到最大值；图像采集模块主要使用去除红外截止滤波片的高清红外工业相机，该相机的最佳光谱感应范围包含所选的9个LED灯珠的中心波长；图像分析模块主要执行客观图像质量评估算法。系统执行时，STC89C51单片机发射周期为T的脉冲波来驱动9种不同波长的LED分时点亮。然后，计算机平台调用高清红外相机模组，以相匹配的间隔捕获多光谱图像。在系统拍摄参数优化实验中，本文采用模糊度和清晰度评价指标对所获得的多光谱图像从相机拍摄时间间隔、相机拍摄距离和光照强度3个角度进行质量评估，进而获得较优的系统成像参数。结果 通过改变系统拍摄参数，对3个场景下的不同拍摄条件所获取的多光谱图像质量进行评估，结果显示：对于本文所搭建的多光谱成像系统，相机拍摄时间间隔与LED灯珠频闪周期同步，拍摄距离为25 mm，光照强度为45 Lux下成像质量相对较好。结论 本文设计并搭建的基于脉冲调制的低成本便携式多光谱成像系统成本低、操作难度小、结构简单、成像质量较好、成像速度较快，可以满足多光谱成像系统大规模推广使用的要求。此外，本文的系统设计方法、设计思路和实验方案等可以为后续研究提供借鉴。     

Operational land cover change detection using change vector analysis

This research study introduces the use of a change detection and classification algorithm that relies on the change vector analysis (CVA) method. Its implementation aims to ensure adequate response to operational production needs and allow optimized data processing over extended and environmentally complex areas. Automatic change class labelling relies on the use of a (3n+2)‐dimensional feature space, where n denotes the number of sensor bands. Such enhanced feature space allows for a finer and more accurate definition of change classes of the ‘from‐to’ type. Moreover, and to efficiently address the problem of change area overestimation, the proposed method takes into account specific evidence derived from the pixel's geographic neighbourhood, the latter defined as a 3×3 pixel kernel. The performance of this integrated algorithmic approach has been tested and validated in the framework of the CORINE Land Cover‐Greece 2000 and the ESA/GSE Forest Monitoring projects in three test sites located in the outskirts of the city of Ptolemais, Thasos island and the suburbs of Athens in Greece. Its implementation in such highly fragmented and dynamically changing landscape environments has resulted in qualified and accurate land cover change maps, achieving an overall level of classification accuracy of 88–96%. Compared to visual image interpretation, the method requires half the effort. In conclusion, the proposed method has proved effective and can be recommended for use in the framework of operational projects.     

Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation

ContextTopic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have demonstrated success in mining software repository tasks. Understanding software change messages described by the unstructured nature-language text is one of the fundamental challenges in mining these messages in repositories.ObjectiveWe seek to present a novel automatic change message classification method characterized by semi-supervised topic semantic analysis.MethodIn this work, we present a semi-supervised LDA based approach to automatically classify change messages. We use domain knowledge of software changes to make labeled samples which are added to build the semi-supervised LDA model. Next, we verify the cross-project analysis application of our method on three open-source projects. Our method has two advantages over existing software change classification methods: First of all, it mitigates the issue of how to set the appropriate number of latent topics. We do not have to choose the number of latent topics in our method, because it corresponds to the number of class labels. Second, this approach utilizes the information provided by the label samples in the training set.ResultsOur method automatically classified about 85% of the change messages in our experiment and our validation survey showed that 70.56% of the time our automatic classification results were in agreement with developer opinions.ConclusionOur approach automatically classifies most of the change messages which record the cause of the software change and the method is applicable to cross-project analysis of software change messages.