Spectral Change Vectors Research Articles

Dynamic monitoring and restorability evaluation of alpine wetland in the eastern edge of Qinghai–Tibet Plateau

Accurate monitoring of alpine wetland areas and scientific evaluation of alpine wetland restorability play important roles in revealing the changing mechanism of the ecological environment. However, because of the complexity and lack of spatial data, few studies on evaluation index system of restorability of alpine wetland considered wild animal habitat, such as habitat suitability index of black necked crane. Based on Landsat images of seven periods from 1990 to 2020, this study adopts the methods of spectral mixture analysis and change vector analysis to quantitatively demonstrate the variations and distribution patterns of alpine wetland ecosystem in the eastern edge of Qinghai–Tibet Plateau. The study focuses on determining the abundance of water, vegetation and soil, which the interannual variations represent as the shapes of ‘√’, mirror ‘N’ and normal ‘N’, respectively. In terms of natural ecological condition and human activity interference, we construct a comprehensive evaluation model of restorability of alpine wetland, separate the restorability district and construct the Bayesian network model to discuss the causal relationship between the restorability value and evaluation factor. Results show that the regions of alpine wetland in the study area changed most drastically from 1995 to 2000 and from 2000 to 2005. In addition, the total areas classified as suitable, relatively suitable and suitable for restoration account for 63.69 % of the study area, with a large potential for wetland restorability. Moreover, the prioritisation scheme of wetland restoration action includes the valley swamps of Heihe River in the middle, followed by the flat wetlands and grasslands in the south and lastly by uneven wetlands in the north.

A novel unsupervised multiple change detection method for VHR remote sensing imagery using CNN with hierarchical sampling

ABSTRACT Detecting multiple changes from remote sensing imagery is a research hotspot. Very high resolution (VHR) images contain detailed spatial information and thus are often used in multiple change detection (CD). Compared with supervised multiple CD methods, unsupervised methods are more attractive, due to the ability of extracting changes automatically. However, many existing unsupervised methods fail to well adaptively make use of the high-level features relevant to multiple changes in VHR images in some cases. In this paper, a novel unsupervised multiple CD method for VHR images is proposed. First, the magnitude of spectral change vectors (SCVs) is calculated by change vector analysis, and fuzzy c-means clustering is performed to generate the unchanged and candidate changed samples. Secondly, the candidate changed samples are further clustered based on the direction of SCVs, and multiple changed samples are selected using a local window. Finally, image patches composed of neighbourhood areas of the generated samples are fed into a convolutional neural network (CNN) for training, and the multiple change map is obtained by the trained CNN. Experiments were performed on four data sets, and results indicated that the proposed unsupervised multiple CD approach outperformed some other state-of-the-art methods.

Improving Estimates of Soil Salt Content by Using Two-Date Image Spectral Changes in Yinbei, China

Soil salt content (SSC) is normally featured with obvious spatiotemporal variations in arid and semi-arid regions. Space factors such as elevation, temperature, and spatial locations are usually used as input variables for a model to estimate the SSC. However, whether temporal patterns of salt-affected soils (identified as temporal spectral patterns) can indicate the SSC level and be applied as a covariate in a model to estimate the SSC remains unclear. Hence, temporal changes in soil spectral patterns need to be characterized and explored as to their use as an input variable to improve SSC estimates. In this study, a total of 54 field samples and a time-series of Sentinel-2 multispectral images taken at monthly intervals (from October 2017 to April 2018) were collected in the Yinbei area of western China. Then, two-date satellite images were used to quantify significant spectral changes over time using spectral change vector analysis, and four two-date-based index methods were used to characterize soil spectral changes. Lastly, the optimal two-date-based spectral indices and multispectral bands were used as input variables to build the estimation models using a random forest algorithm. Results showed that the two-date-based spectral index could be applied as an input variable to improve the accuracy of SSC estimation at a regional scale. Temporal changes in salt-induced spectral patterns can be indicated by the band difference in the wavelength range from 400 nm to 900 nm. Three two-date-based indices designated as D28a (i.e., the band difference between band 2 from an image acquired in April 2018 and band 8a from an image acquired in December 2017), D22, and D28 were the optimal parameters for characterizing salt-induced spectral changes, which were dominated by the total brightness, chloride, and sulfate accumulation of the soils. The model did not yield satisfactory estimation results (RPD = 1.49) when multispectral bands were used as the input variables. Multispectral bands coupled with two two-date-based indices (D22 and D28a) used as the input variables produced the best estimation result (R2 = 0.92, RPD = 3.27). Incorporating multispectral bands and two-date-based indices into the random forest model provides a remotely-sensed strategy that effectively supports the monitoring of soil salt content.

A Novel Change Detection Framework in Urban Area Using Multilevel Matching Feature and Automatic Sample Extraction Strategy

Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this article. First, a novel multilevel matching feature is presented, combining structural invariant features with multiscale dense matching features to comprehensively describe the invariant properties of ground objects at different levels. Second, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster–Shafer evidence theory and L1-norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers, respectively ( k -nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and misregistration. The results demonstrate that the proposed multilevel matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene.

Development of integrated wetland change detection approach: In case of Erdos Larus Relictus National Nature Reserve, China

Wetlands are threatened by the global warming and the human exploitation pressure, and have been shrinking quickly in recent years. Timely and accurate wetland area change detection is the primary task for wetland conservation and restoration. The objective of this study is to develop an integrated change detection approach which integrates the advantages of spectral mixture analysis (SMA) and change vector analysis (CVA) for the change identification of wetland dynamics. In the proposed approach, water, vegetation and soil fractions of wetlands were derived by SMA; then, the detailed change information (including change magnitude and 12 change direction categories) were calculated through CVA. The proposed approach was applied for the wetlands change in Erdos Larus Relictus National Nature Reserve (ELRNNR), China, using time-series Landsat images during 1977–2017. We found that the wetland faced serious degradation, with water fraction changed to soil (5.79 km2), to vegetation (1.35 km2) and to both soil and vegetation (3.53 km2). From 1977 to 2000, a slight degradation occurred in the northeast edge of Bojiang Lake and a marginal degradation in Bojiang and Houjia Lakes inside the ELRNNR, with water fraction changed to soil and vegetation. During 2000–2010, severe degradation occurred in ELRNNR, and from 2010 to 2017, the wetland was more susceptible to the precipitation change and human activities. Analysis of the result indicated that the long-term drought and effects of mismanagement as well as misuse by human beings were the driving factors of wetland degradation. The proposed approach in this study achieves a higher accuracy than the classification approach to detect wetland change, with the ability to obtain more detailed change information.

Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

Spectrally-Spatially Regularized Low-Rank and Sparse Decomposition: A Novel Method for Change Detection in Multitemporal Hyperspectral Images

Change detection (CD) for multitemporal hyperspectral images (HSI) can be approached as classification consisting of two steps, change feature extraction and change identification. This paper is focused on binary classification of the changed and the unchanged samples, which is the essential case of change detection. Meanwhile, it is challenging to extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI. The corruptions can be characterized as gross sample-specific errors, i.e., outliers, and small entry-wise noise following Gaussian distribution. To address the issue, this paper proposes a novel Spectrally-Spatially (SS) Regularized Low-Rank and Sparse Decomposition (LRSD) model, denoted by LRSD_SS. It decomposes the SCV into three components, a locally smoothed low-rank matrix for the clean change features, a sparse matrix for the outliers and an error matrix for the small Gaussian noise. The proposed method is effective in change feature extraction and robust to noise corruptions as it exploits the underlying data structures of the SCV, especially local spectral-spatial smoothness. It is also efficient since there is a closed-form solution for the feature component in the optimization problem of LRSD_SS. The experimental results in the paper show that the proposed method outperforms several classic methods which only deal with the spectral domain of image samples, as well as some state-of-the-art methods which use both spectral and spatial information

Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection

A novel multiscale morphological compressed change vector analysis (M2C2VA) method is proposed to address the multiple-change detection problem (i.e., identifying different classes of changes) in bitemporal remote sensing images. The proposed approach contributes to extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) method by jointly analyzing the spectral-spatial change information. In greater details, reconstructed spectral change vector features are built according to a morphological analysis. Thus more geometrical details of change classes are preserved while exploiting the interaction of a pixel with its adjacent regions. Two multiscale ensemble strategies, i.e., data level and decision level fusion, are designed to integrate the change information represented at different scales of features or to combine the change detection results obtained by the detector at different scales, respectively. A detailed scale sensitivity analysis is carried out to investigate its impacts on the performance of the proposed method. The proposed method is designed in an unsupervised fashion without requiring any ground reference data. The proposed M2C2VA is tested on one simulated and three real bitemporal remote sensing images showing its properties in terms of different image size and spatial resolution. Experimental results confirm its effectiveness.

Response of fuzzy clustering on different threshold determination algorithms in spectral change vector analysis over Western Himalaya, India

Change detection is a standard tool to extract and analyze the earth’s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis (CVA) have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification (FMLC) as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search (DFPS), interactive trial and error (T&E), and 3×3‒pixel kernel window (PKW). Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA over improved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.

Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery

The severe Sahel catastrophe in 1968–1974 as well as repeated famines and food shortage that have hit many African countries during the 1970s have highlighted the need for further research concerning land degradation and environmental monitoring in arid and semi-arid areas. Land degradation, and desertification processes in arid and semi-arid environment were increased in the last four decades, especially in the developing countries like Sudan. To test to what extent remote sensing and geographical information science (GIS) methodologies and techniques could be used for monitoring changes in arid and semi-arid regions and environment, these methodologies have long been suggested as a time and cost-efficient method. In this frame, spectral Mixture Analysis (SMA), Object-based oriented classification (Segmentation), and Change Vector Analysis are recently much recommended as a most suitable method for monitoring and mapping land cover changes in arid and semi-arid environment. Therefor the aim of this study is to use these methods and techniques for environmental monitoring with emphasis on desertification and to find model that can describe and map the status and rate of desertification processes and land cover changes in semi-arid areas in White Nile State (Sudan) by using multi-temporal imagery of the Landsat satellite TM (1987), TM (2000), and ETM+ (2014) respectively. The paper also discusses and evaluates the efficiency of the adapted methodologies in monitoring the land degradation processes and changes in the arid and semi-arid regions.

Unsupervised Change Detection Based on Sequential Spectral Change Vector Analysis for Updating Land Cover Map

Unsupervised Change Detection Based on Sequential Spectral Change Vector Analysis for Updating Land Cover Map

Strategies Combining Spectral Angle Mapper and Change Vector Analysis to Unsupervised Change Detection in Multispectral Images

Change vector analysis (CVA) has been widely used in change detection of multitemporal multispectral images, whereas the use of a spectral angle mapper (SAM) is always ignored. CVA and SAM describe the difference from the perspective of vector magnitude and direction, respectively. Combining CVA and SAM can fully utilize the spectral vector and obtain a better change map. In this context, two strategies have been proposed: 1) a novel hybrid feature vector is constructed using a spectral angle and a change vector, which is utilized in SAM and CVA, respectively, and 2) a novel difference image is generated by using the autoadapted fusion strategy, which fuses the difference images acquired by utilizing CVA and SAM. During the fusion process, the autoadapted weight is defined by employing the entropy of the difference image. Experimental results on both simulated images and multitemporal multispectral images validate the effectiveness of the proposed methods.

Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images

This paper presents an effective semiautomatic method for discovering and detecting multiple changes (i.e., different kinds of changes) in multitemporal hyperspectral (HS) images. Differently from the state-of-the-art techniques, the proposed method is designed to be sensitive to the small spectral variations that can be identified in HS images but usually are not detectable in multispectral images. The method is based on the proposed sequential spectral change vector analysis, which exploits an iterative hierarchical scheme that at each iteration discovers and identifies a subset of changes. The approach is interactive and semiautomatic and allows one to study in detail the structure of changes hidden in the variations of the spectral signatures according to a top-down procedure. A novel 2-D adaptive spectral change vector representation (ASCVR) is proposed to visualize the changes. At each level this representation is optimized by an automatic definition of a reference vector that emphasizes the discrimination of changes. Finally, an interactive manual change identification is applied for extracting changes in the ASCVR domain. The proposed approach has been tested on three hyperspectral data sets, including both simulated and real multitemporal images showing multiple-change detection problems. Experimental results confirmed the effectiveness of the proposed method.

Application of hyperspectral remote sensing for flower mapping in African savannas

We tested the suitability and accuracy of hyperspectral data to produce the first African flowering and short-term floral cycle map. The spatial distribution and abundance, as well as the floral cycle, of melliferous plants are of utmost importance for evaluating pollination effects and to understand the relationship between melliferous plants in the landscape and the quantity and quality of bee keeping products. For a study site in Kenya, airborne AISA/Eagle hyperspectral data with 60cmpixel resolution (400 to 990nm spectral ranges) was captured in January 2014, at the beginning of the prime flowering period, and during the prime flowering period in February 2013. Aerial digital imagery with 10cmpixel size and Smartphone captures in the field were used for reference data collection. The flowering species were grouped into functional flowering plant groups. Linear spectral unmixing and Change Vector Analysis (CVA) were used on the bi-temporal AISA/Eagle data to produce a hard cover map showing the spatial distribution, abundance and short-term flowering cycle of melliferous plants. Overall accuracies were slightly higher in the February 2013 imagery at the prime flowering period; all flowering plant groups together (“All”) could be mapped with an overall accuracy of 83% (n=512). The “White forbs” flowering plant group was most accurately mapped in both AISA/Eagle acquisition dates. Based on Duncan's inter-class similarity test, the “White forbs” group was also most distinct from other flowering plant groups. There is a need to investigate the effect of spectral endmember variability and upscaling options for space-borne monitoring of the floral cycle at key sites in Africa. Floral cycle maps can help decision makers and bee keepers to understand how bee colonies interact with the floral environment and what to expect from an apiary in terms of honey flow.

Impact of Topography on Accuracy of Land Cover Spectral Change Vector Analysis Using AWiFS in Western Himalaya

The present paper discusses the impact of topography on accuracy for land cover classification and “from-to class change using improved spectral change vector analysis suggested by Chen et al. (2003). Two AWiFS sensor images of different dates are used. Double Window Flexible Pace Search (DFPS) is used to estimate threshold of change magnitude for change/no change classes. The topographic corrections show accuracy of 90% (Kappa coefficient 0.7811) for change/no change area as compared to 82% (Kappa coefficient 0.6512) in uncorrected satellite data. Direction cosines of change vector for determining change direction in n-dimensional spectral space is used for image classification with a minimum distance categorizing technique. The results of change detection are compared (i) Improved CVA with conventional two bands CVA and (ii) Improved CVA before and after topographic corrections. The improved CVA with topographic correction consideration using slope match show maximum accuracy of 90% (Kappa coefficient 0.83) as compared to conventional CVA which show maximum accuracy of 82% (Kappa coefficient 0.6624). The overall accuracy of ”from- to class using improved CVA increases from 86% (Kappa coefficient 0.7817) to 90% (Kappa coefficient 0.83) after topographic corrections. The improved CVA with proper topographic corrections is found to be effective for change detection analysis in the rugged Western Himalayan terrain.

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.

Change Detection from Landsat TM Images Using Change Vector Analysis

Change detection is a process of extracting, analyzing, and defining change information from remote sensing imageries. At present, remote sensing change detection methods are mainly classified into two categories, one based on the spectral characteristics of the type of approach, and the other is spectral change vector analysis. In this paper, a simplified threshold variable step-size search which can be used on determining changes in the threshold vector is adopted, as well as the supervised classification technique in the direction cosine space with the type of focal point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city.

Spectral change directions of multispectral subpixels in image fusion

In an image fusion process, the spatial detail in a panchromatic pixel is injected into the corresponding n-band multispectral (MS) subpixel to yield a synthetic pixel. The synthetic pixel can be regarded as the sum of three terms: the MS subpixel, a shift term, and a product of the spatial detail and an n-dimensional (n-D) spectral change vector. In this paper, the spectral change vector directions in some current image fusion methods are characterized and classified. As the spectral development of subpixels with the improvements in spatial resolution in imaging is analogous to the spectral change of MS subpixels with the improvements in spatial resolution in image fusion, the former is used as a reference to examine the spectral change vector directions in the current image fusion methods. Moreover, image haze and unmixing of mixed MS subpixels are highlighted as two aspects needing attention for further improvement in fusion quality.

Change Detection Using Change Vector Analysis from Landsat TM Images in Wuhan

Abstract Spectral Change Vector Analysis (CVA) is based on multi-temporal images. In this paper, a dichotomy search which can be used on detecting changes in the threshold vector is adopted. Meanwhile, a supervised classification technique is used in the direction cosine space with the type of central point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city.

A Context-Sensitive Technique Robust to Registration Noise for Change Detection in VHR Multispectral Images

This paper presents an automatic context-sensitive technique robust to registration noise (RN) for change detection (CD) in multitemporal very high geometrical resolution (VHR) remote sensing images. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. This information is jointly exploited with the spatial context information contained in the neighborhood of each pixel for generating the final CD map. The spatial context information is modeled through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). Experimental results obtained on real VHR remote sensing multitemporal images confirm the effectiveness of the proposed technique.