Gradual land cover change detection based on multitemporal fraction images

This study proposes a new approach to change detection in remote sensing multi-temporal image data. Rather than allocating pixels to one of two disjoint classes (change, no-change) which is the approach most commonly found in the literature, we propose in this study to define change in terms of degrees of membership to the class change. The methodology aims to model images depicting the natural environment more realistically, taking into account that changes tend to occur in a continuum rather than being sharply distinguished. To this end, a sub-pixel approach is implemented to help detect degrees of change in every pixel. Three experiments employing the proposed approach using synthetic and real image data are reported and their results discussed.     

A genetic expectation-maximization method for unsupervised change detection in multitemporal SAR imagery

In this paper, we present a novel, automatic and unsupervised change-detection approach to the analysis of single-channel single-polarization multitemporal SAR images. The statistical parameters of the changed and unchanged classes, which are assumed to follow a generalized Gaussian (GG) distribution in the analysed log-ratio image, are explicitly estimated by the expectation-maximization (EM) algorithm initialized with a robust strategy based on genetic algorithms (GAs). In addition, the proposed approach integrates two further processing capabilities. The first one intends to cope with the problem of the automatic detection of multiple changes in the scene. This is carried out by modelling the log-ratio image histogram with a multimodal GG mixture whose number of components is estimated basing on the Bayesian information criterion (BIC). The second processing capability allows exploitation of spatial contextual information in the change detection process through a Markovian formulation. Results obtained on both simulated and real data are reported and discussed.     

整型推理量化CNN的SAR图像跨域变化检测

目的 合成孔径雷达（synthetic aperture radar,SAR）特有的成像优势使得SAR图像变化检测在民用和军事领域有着广泛的应用场景,但实际应用中对SAR图像的变化区域进行标注既耗时又昂贵,而且现有的变化检测方法复杂度较高,无法满足实时、快速检测的需求。对此,提出了一种基于整型推理量化卷积神经网络的SAR图像跨域变化检测方法（integer inference-based quantization convolutional neural network,IIQ-CNN）。方法 该方法研究了不同场景之间的跨域变化检测问题,即利用已有标记的源域数据对未知的目标域数据进行检测;设计了同时使用时相图和差异图的样本构建方法,既避免了检测结果对差异图的过分依赖,又能充分利用差异信息和时相图与差异图之间的共享信息,提高检测精度;并且在变化检测任务中首次引入整型推理量化技术,对深度网络模型进行模拟量化,减小模型复杂度并加速推理时间。结果 在４组真实的SAR图像数据集上进行实验,从检测性能上看,IIQ-CNN与其他CNN方法相比,Kappa系数提高了4.23%～9.07%;从量化能力上看,对IIQ-CNN分别进行16、8和4位量化,仅在4位量化时检测结果有较明显下降,在16和8位量化时,模型都保持了较好的检测性能,并且推理时间明显减少。结论 本文方法有效解决了伪标签质量对变化检测性能的影响,实现了加速推理的同时较好地保持模型检测精度的目的,促进了变化检测算法在嵌入式设备中的应用。     

Novel class-relativity non-local means with principal component analysis for multitemporal SAR image change detection

One of the main problems for change detection in multitemporal synthetic aperture radar (SAR) images is the presence of speckle noise, since it degrades the image quality significantly and may hide important details in the image. In this article, we investigate a novel class-relativity non-local means (CRNLM) algorithm that reduces the effect of speckle noise in the principal component analysis (PCA) feature space for SAR image change detection. Note that the non-local means averaging process is particularly true when the assumed noise model is additive. Thus, we adopt the difference image produced by the ratio image expressed in logarithmic scale and then transform it onto PCA space. This is done so that its signal energy is concentrated, and the noise spreads over the whole PCA space and is additive. A task-dependent CRNLM algorithm is applied to the PCA transformed data set so as to combine local and non-local geometries and capture the robustness to noise. The idea is based on the assumption that non-local similar patches have similar class structures. Visual and quantitative results obtained on real multitemporal SAR image data sets confirm the effectiveness of this method as compared with several state-of-the-art techniques.     

Object-based land cover change detection for cross-sensor images

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image–object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.     

Objects based change detection in a pair of gray-level images

The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along gray-level technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.     

A coupling translation network for change detection in heterogeneous images

Based on the images acquired through different sensors, change detection is much more challenging than those based on homogeneous images. The main reason behind it is that the heterogeneous image-pair cannot be directly compared in original observation space due to their distinct statistical properties. In order to detect the changes, we establish a coupling variational autoencoder (VAE) to transform the heterogeneous images into a shared-latent space, where they have more consistent representations and hence the prior changed regions can be highlighted by direct comparison. And based on the shared space, we build coupled generative adversarial networks (GANs) associated with the coupling VAE to translate the heterogeneous images into homogeneous, from which more accurate change detection results can be obtained in their common observation spaces. The proposed framework is totally unsupervised, and the experimental results on real heterogeneous data sets demonstrate its superiority over some other existing algorithms.     

基于敏感特征向量的SAR图像灾害变化检测技术

为了解决阴雨云雾条件下光学遥感图像的应用局限性问题,针对典型的四类地表变化（堰塞湖、滑坡泥石流、部分倒塌建筑和严重倒塌建筑）分析SAR图像灰度和纹理特征的敏感程度,并提出敏感特征向量的概念;以综合利用了灰度差值和纹理差值的敏感特征向量作为评判因子,结合主成分分析技术和K均值聚类技术,提出了新的SAR图像灾害变化检测算法。该方法算法简单,检测效果好,并用两组ALOS SAR实验数据进行了证实。     

An end-user-oriented framework for the classification of multitemporal SAR images

In this article, we present an end-user-oriented framework for multitemporal synthetic aperture radar (SAR) data classification. It accepts as input the recently introduced Level-1α products, whose peculiarities are a high degree of interpretability and increased class separability with respect to single greyscale images. These properties make the Level-1α products very attractive in the application of simple supervised classification algorithms. Specifically, (1) the high degree of interpretability of the maps makes the training phase extremely simple; and (2) the good separation between classes gives excellent results using simple discrimination rules. The end product is a simple, fast, accurate, and repeatable framework.     

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

A multiobjective fuzzy clustering method for change detection in SAR images

On account of the presence of speckle noise, the trade-off between removing noise and preserving detail is crucial for the change detection task in Synthetic Aperture Radar (SAR) images. In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. The change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. We optimize the two constructed objective functions simultaneously by using a multiobjective fuzzy clustering method, which updates the membership values according to the weights of the two objectives to find the optimal trade-off. The proposed method obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems. Experiments conducted on real SAR images demonstrate the superiority of the proposed method.     

Histogram thresholding for unsupervised change detection of remote sensing images

The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.     

A change detection method using spatial-temporal-spectral information from Landsat images

ABSTRACT

Remote sensing data and techniques are reliable tools for monitoring land cover and land-use change. For time-series change detection algorithms, detecting the breakpoints accurately is the key element. However, the current state-of-art algorithms are vulnerable to cloud/cloud shadow or noises in the time-series imagery. The objective of this study is to develop a new method to detect land cover change using Landsat imagery by integrating temporal, spectral and spatial information to increase the accuracy of breakpoints detection. In the temporal dimension, the time-series model is decomposed into seasonality and trend. Due to different land cover types corresponding to different seasonal characteristics, breakpoints exist only in the seasonal component. In the spectral dimension, two-step judgement is applied. The first judgement detects a change when the seasonal breakpoint positions are the same in different spectral bands. The second judgement involves detecting a changed pixel when the classification result indicates different types on either side of the breakpoint. In the spatial dimension, neighbour information is utilized to control the false-positive rate. Experimental results using all available Landsat images acquired between 2001 and 2006 in Kansas City, US, illustrate the effectiveness and stability of the proposed approach. All pixels were used for assessing the classification and change detection accuracy compared with National Land Cover Database products. The overall accuracy of classification into eight categories was about 81% and the accuracy of change detection was 88%. Maps of timing of breaks and change times are also provided in this article.     

The estimation of marshland degradation in southern Iraq using multitemporal Landsat TM images

Characterization of the marshlands of southern Iraq using Landsat TM data enabled an estimation of the rate at which disruption of water supply has led to the collapse of the ecosystem. Image classification techniques were used to estimate vegetation distribution using an NDVI image, and the areal coverage of water, using bands 1 to 5 of the TM. The Al Amarah marsh to the north of the Euphrates River has seen a reduction of 90 per cent in its areal extent between 1992 and 1994. South of the Euphrates river the Hawr al Hammar marsh has undergone a similar reduction in areal extent between 1992 and 1994, but the collapse of vegetation occurred predominantly between 1993 and 1994. Vegetation collapse appears to be linked to the completion of drainage projects that prevent the influx of nutrient rich spring flood waters to the marshes. East of the Tigris river, the Hawr al Hawizah marsh was observed to have undergone a decline of 40 per cent in the amount of vegetation present between 1992 and 1994. Inundation northwest of a series of barrages around the northern perimeter of the Hawr al Hawizah marsh in 1993 and 1994 suggests that disruption of the influx of water from the Nahr al Musharrah river is the cause of vegetation decline.     

Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images

Impervious surface is a key indicator of urban environmental quality and degree of urbanization. Therefore, estimation and mapping of impervious surfaces by using remote sensing digital images has attracted increasing attention recently. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and image analysis must take into account this environmental characteristic. In this paper, three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, acquired on 3 October 2000, 16 June 2001 and 5 April 2004, respectively, were used to test the seasonal sensitivity of impervious surface estimation. The study area was the city of Indianapolis (Marion County), Indiana, USA. Linear spectral mixture analysis (LSMA) was applied to generate high-albedo, low-albedo, vegetation and soil fraction images (endmembers), and impervious surfaces were then estimated by adding high- and low-albedo fraction images. In addition, land use/land cover (LULC) and land surface temperature (LST) maps were generated and used to create image masks to remove non-impervious pixels. The accuracy of the impervious surface maps was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. Three accuracy indicators, the root mean square error (RMSE), mean average error (MAE) and correlation coefficient (R ²), were calculated and compared to analyse the seasonal sensitivity of impervious surface estimation. Our results indicate that vegetation phenology has a fundamental impact on impervious surface estimation. The summer (June) image was better for impervious surface estimation than the spring (April) and autumn (October) images. The LULC and LST image masks can significantly increase the accuracy of impervious surface estimation. The mean LST was found appropriate to be set as the threshold for the various image masks. A summer image was most appropriate because there was full growth of vegetation, and mapping of impervious surfaces was more effective with a contrasting spectral response from green vegetation. The mixing space, based on the four endmembers, was perfectly three-dimensional. By contrast, there was significant amount of bare soil and ground and non-photosynthetic vegetation in the spring and autumn images. Plant phenology caused changes in the variance partitioning and impacted the mixing space characterization, leading to a less accurate estimation of the impervious surfaces.     

模糊Bayes 理论在遥感影像变化检测中的应用

针对传统基于Bayes 决策规则的遥感影像变化检测方法中参数估计的不足以及分类过程中的硬划分问题,采用动态更新变化和未变化两类像元模糊子集的方法,实现对两类像元模糊子集中参数的动态更新,利用估计参数获得各子集的后验概率函数,再将后验概率函数转化为模糊子集的模糊隶属函数,从而获得各子集的指标函数,根据指标函数对影像中未分类的像元值进行判断,实现遥感影像的变化区域提取。实验结果表明：与现有的基于Bayes 决策规则的遥感影像变化检测方法及ERDAS 软件生成结果相比,提出的方法具有更好的变化检测精度。     

结合结构相似度的自适应多尺度SAR图像变化检测

目的 结合高斯核函数特有的性质,提出一种基于结构相似度的自适应多尺度SAR图像变化检测算法。方法 本文提出的算法包括差异图像获取、高斯多尺度分解、基于结构相似性的最优尺度选择、特征矢量构造以及模糊C均值分类。首先,通过对多时相SAR图像进行对数比运算获取差异图像,然后,利用基于图像的结构相似度估计高斯多尺度变换的最优尺度,继而在该最优尺度参数下逐像素构建变化检测特征矢量,最后通过模糊C均值聚类方法实现变化像素与未变化像素的分离,生成最终的变化检测结果图。结果 在两组真实的SAR图像数据上测试本文算法,正确检测率分别达到0.9952和0.9623,Kappa系数分别为0.8200和0.8540,相比传统算法有了较大的提高。结论 本文算法充分利用了尺度信息,对噪声的鲁棒性有所提高。实测SAR数据的实验结果表明,本文算法可以智能获取最优分解尺度,显著提高了SAR图像变化检测性能。     

Wetland change detection on the Kafue Flats,Zambia, by classification of a multitemporal remote sensing image dataset

The paper describes a remote sensing change detection approach used to assess change on a section of the Kafue Flats floodplain wetland system in southern Zambia, which is under the pressures of reduced regional rainfall and damming and water abstraction by man. Four images from September 1984 (Landsat MSS), 1988 (Landsat MSS), 1991 (Landsat TM) and 1994 (Landsat TM) were used. Being near-anniversary images, the change detection error introduced by mere seasonal differences was minimized. Following atmospheric correction of the reference (1994) image, the images were radiometrically normalized and geometrically registered to a common map projection. Each image was separately classified into categories of open water, dense green vegetation, sparse green vegetation, very sparse green vegetation, dry and burnt land. Similar, supervised maximum likelihood classification procedures were employed on all images. The classified images produced were analysed for change in each land-cover category by overlaying them in a Geographic Information System (GIS) framework. The results indicated spatial reduction in area of dense green vegetation in upstream sections of the wetland. Inter-image changes in this land-cover class could be explained by the variations in the timing of regulated flood events on the Kafue Flats. The methodology employed appears to be applicable to monitoring southern Africa's inland wetland systems.     

Information fusion techniques for change detection from multi-temporal remote sensing images

In order to investigate the impacts of different information fusion techniques on change detection, a sequential fusion strategy combining pan-sharpening with decision level fusion is introduced into change detection from multi-temporal remotely sensed images. Generally, change map from multi-temporal remote sensing images using any single method or single kind of data source may contain a number of omission/commission errors, degrading the detection accuracy to a great extent. To take advantage of the merits of multi-resolution image and multiple information fusion schemes, the proposed procedure consists of two steps: (1) change detection from pan-sharpened images, and (2) final change detection map generation by decision level fusion. Impacts of different fusion techniques on change detection results are evaluated by unsupervised similarity metric and supervised accuracy indices. Multi-temporal QuickBird and ALOS images are used for experiments. The experimental results demonstrate the positive impacts of different fusion strategies on change detection. Especially, pan-sharpening techniques improve spatial resolution and image quality, which effectively reduces the omission errors in change detection; and decision level fusion integrates the change maps from spatially enhanced fusion datasets and can well reduce the commission errors. Therefore, the overall accuracy of change detection can be increased step by step by the proposed sequential fusion framework.