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

Zhuang, Huifu; Deng, Kazhong; Fan, Hongqi; Yu, Mei

doi:10.1109/lgrs.2016.2536058

Cited by 88 publications

(58 citation statements)

References 25 publications

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“…Both bitemporal multispectral images have been co-registered and radiometrically corrected beforehand. The change detection results from the proposed method were compared with those from four unsupervised change detection methods, namely the EM-CVA method [3], the robust chisquared transform (RCST) method [20], the FCM algorithm combined with Markov random field (FCMMRF) on the MDI [10], and the combination of MDI and SAI (hybrid feature vector, HFV) applied with KittlerIllingworth threshold [14]. In the proposed method, the iteration number of optimization is set to 50, the convergency criterion is set to V new − V old < 0.0001 and the value of δ is 0.1.…”

Section: Methodsmentioning

confidence: 99%

“…It can provide monitoring information of change for government and has been applied to many domains such as forestry monitoring, natural diaster monitoring, and the urban development [1,2]. In general, change detection technique can be divided into two main categories: unsupervised [3][4][5][6][7][8][9][10][11][12][13][14] and supervised change detection methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Change detection of bitemporal multispectral images based on FCM and D-S theory

Shi

Gao

Shou-yun³

2016

EURASIP J. Adv. Signal Process.

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In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Change detection of bitemporal multispectral images based on FCM and D-S theory

Shi

Gao

Shou-yun³

2016

EURASIP J. Adv. Signal Process.

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“…If the pixel value of d' is not similar enough to that of d, then d' is substituted in the next CEM, a new desired target is obtained. This is repeated until this and the last Spectral Angle Mapper [46] are smaller than a value θ, and then the current target d is exported. The threshold of spectral angle was tested continuously, and the threshold was set as 0.003. segmented by using Otsu's method until the number of result pixels is 2-3%, which is the target pixel with the highest probability of the sprout.…”

Section: Optimal Signature Generation Process (Osgp)mentioning

confidence: 99%

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

et al. 2018

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Abstract:Leaf maturation from initiation to senescence is a phenological event of plants that results from the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful for the diagnosis of tree growth, tree stress, and even climatic change. This paper applies Constrained Energy Minimization (CEM), which is a hyperspectral target detection technique to spot grown leaves in a UAV multispectral image. According to the proportion of NGL in different regions, this paper proposes three innovative CEM based detectors: Subset CEM, Sliding Window-based CEM (SW CEM), and Adaptive Sliding Window-based CEM (AWS CEM). AWS CEM can especially adjust the window size according to the proportion of NGL around the current pixel. The results show that AWS CEM improves the accuracy of NGL detection and also reduces the false alarm rate. In addition, the results of the supervised target detection depend on the appropriate signature. In this case, we propose the Optimal Signature Generation Process (OSGP) to extract the optimal signature. The experimental results illustrate that OSGP can effectively improve the stability and the detection rate.

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“…Based on the differences of 'unit' in prior research [16][17][18], in the current study, 'unit' is the fundamental analysis scale for inprocessing, and it usually relates to sub-pixel, pixel and object. The developed approaches can be classified into two, namely, pixel-based change detection (PBCD) and object-based change detection (OBCD) approaches [19][20][21]. The PBCD approach usually relates to two steps: Generating the change magnitude image (CMI) and providing a binary threshold to…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

Liu²,

Benediktsson

et al. 2018

Remote Sensing

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To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches. divide the CMI into a binary change detection map (BCDM). Numerous methods can be used to generate the CMI, such as difference and ratios [22,23], change vector analysis [19] and spectral gradient difference [18]. In addition, a binary threshold is necessary for distinguishing whether a pixel in CMI is changed or unchanged, such as the most commonly used Otsu [20,24] and expectation maximisation [25]. Despite the many traditional PBCD approaches applied in practice, numerous existing PBCD approaches cannot provide satisfactory detection results with the use of VHR remote sensing images because, although superior in visual performance, these images are usually insufficient in the spectra [26][27][28]. Contextual information around a pixel is usually considered to solve this problem; for example, Lv et al. proposed an LCCD approach for VHR images based on adaptive contextual information [20], Zhang et al. promoted a level set evolution with local uncertainty constraints (LSELUC) [4] for unsupervised change detection [4] and Celik developed a principal component analysis and k-means clustering (PCA-Kmeans) approach [29].Apart from the aforementioned PBCD methods, OBCD is widely used for LCCD while employing remote sensing images with high spatial resolution. In general, a pre-step for the OBCD method is multi-scale segmentation, which generates a group of multi-scale segments. The designation for detecting land-cover change is based on the candidate segments. For example, Silveira et al. applied an object-based LCCD approach to detect Brazilian seasonal savannahs through a geostatistical object feature [30], and Dronova et al. presented an object-based LCCD method for monitoring wetland-cover type changes in Poyang Lake region, China [31]. Despite the advantages...

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

Cited by 88 publications

References 25 publications

Change detection of bitemporal multispectral images based on FCM and D-S theory

Change detection of bitemporal multispectral images based on FCM and D-S theory

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

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