Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Lv, Zhiyong; Liu, Tongfei; Wan, Yat-wah; Benediktsson, Jón Atli; Zhang, Xiaokang

doi:10.3390/rs10030472

Cited by 50 publications

(42 citation statements)

References 60 publications

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“…When fusing based on the wDST, the segmentation image was generated by allocating the scale parameter as 500. To evaluate the performance of the proposed OBCD method, three PBCD results (i.e., CVA [15], IRMAD [40], and PCA [41]) prior to fusion and their extended OBCD results using the majority voting technique [28] were generated for a comparison purpose. Additionally, three PBCD results were combined with the dual majority voting technique to generate the OBCD result [29].…”

Section: Experiments On the First Datasetmentioning

confidence: 99%

“…Instead of using a pixel as the basic unit for CD, using an object, which is a group of pixels that are spatially adjacent and spectrally similar to each other [23], as a basic unit can be a solution for minimizing problems with the VHR image CD [24]. Object-based change detection (OBCD) extracts meaningful objects by segmenting input images and, thus, is consistent with the original idea of using CD to identify differences in the state of an observed object or phenomenon [1,25,26].Based on our review of the literature, the OBCD methods can be categorized into two groups: (1) fusing spatial features, which takes into consideration of the texture, shape, and topology features of the objects, in the process of change analysis [23,24,27]; and (2) utilizing the object as the process unit to improve the completeness and accuracy of the final result [28][29][30]. Many studies have focused on using the spatial features of the objects.…”

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confidence: 99%

“…Based on our review of the literature, the OBCD methods can be categorized into two groups: (1) fusing spatial features, which takes into consideration of the texture, shape, and topology features of the objects, in the process of change analysis [23,24,27]; and (2) utilizing the object as the process unit to improve the completeness and accuracy of the final result [28][29][30]. Many studies have focused on using the spatial features of the objects.…”

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Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

et al. 2020

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Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.

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Section: Experiments On the First Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

et al. 2020

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“…To quantitatively evaluate the accuracy and performance of each approach, the following three standard measures, which have been used in published literature [42], were employed: (1) false alarm rate (FA): FA = N uc N TC × 100%; (2) missed alarm rate (MA): MA = N cu N TU × 100%; and (3) total error (TE): TE = N uc +N cu N TC +N TU × 100%, where N uc is the number of unchanged pixels detected as change pixels, N TC is the total number of changed pixels within THE ground reference map; N cu is the number of changed pixels detected as unchanged pixels; and N TU is the total number of unchanged pixels within the ground reference map.…”

Section: Evaluation Measurementsmentioning

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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“…In recent decades, various LCCD techniques have been developed and applied in practice [23][24][25][26][27][28]. Two main steps are usually related to these methods, i.e., the generation of a change magnitude image (CMI) and the use of a binary threshold to divide the CMI into a binary change detention map (BCDM).…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Liu

Zhang

et al. 2018

Remote Sensing

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Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.

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Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Cited by 50 publications

References 60 publications

Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

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

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

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