Pixel-based Change Detection Method Research Articles

Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Object-based change detection for vegetation disturbance and recovery using Landsat time series

ABSTRACT Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, “object-based change detection of trends in disturbance and recovery” (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000–2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user’s accuracy and removed salt-and-pepper noise. Spatial–temporal maps of vegetation change showed that the recovery area was 571.27 km2 while the disturbed area was 297.65 km2, accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time.

Deep change feature analysis network for observing changes of land use or natural environment

Change detection on surface of earth plays an important role in global-scale pattern of climate and biogeochemistry of the world, which helps to comprehend the connections and associations between human and nature. Remote Sensing and Geographic Information Systems can possibly provide accurate data in regards to land use and land cover changes. However, pixel-based change detection methods are limited in suppressing outliers and noise; they often fail to process remote sensing images with high spatial-/spectral-resolution. To conquer these drawbacks, a superpixel-level change detection and analysis method is proposed in this paper. Superpixels are the atomic regions gathering pixels with similar property, which will be more efficient and robust than pixels. Deep neural network is a powerful feature learning and classification tool, it can represent superpixel abstractly and classify them robustly. The learning progress of deep architectures includes unsupervised sample selection and supervised feature learning, unsupervised progress aims at selecting training samples for deep neural network, supervised progress aims at learning the representation of superpixels and fine-tuning the whole network to finish classification. Experimental results on multi-temporal images have demonstrated that the proposed approach can handle the task of change detection and analysis effectively and accurately.

Comparison of Pixel-based Change Detection Methods for Detecting Changes on Small Objects

Comparison of Pixel-based Change Detection Methods for Detecting Changes on Small Objects

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures. High spatial resolution remote sensing images can be used to detect subtle vegetation changes. The major objective of this study was to map and quantify forest vegetation changes in a national scenic location, the Purple Mountains of Nanjing, China, using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management. We used Quickbird images acquired in 2004, IKONOS images acquired in 2009, and WorldView2 images acquired in 2015. Four pixel-based direct change detection methods including the normalized difference vegetation index difference method, multi-index integrated change analysis (MIICA), principal component analysis, and spectral gradient difference analysis were compared in terms of their change detection performances. Subsequently, the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes. An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results. The results showed that the MIICA method was the best pixel-based change detection method. And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA. From 2004 to 2009, areas of vegetation gain mainly occurred around the periphery of the study area, while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities, which contributed to 79% of the total area of vegetation loss. During 2009–2015, the greening initiatives around the construction areas increased the forest vegetation coverage, accounting for 84% of the total area of vegetation gain. In spite of this, vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas. We recommend that: (1) a local multi-agency team inspect and assess law enforcement regarding natural resource utilization; and (2) strengthen environmental awareness education.

Change Detection of Polarimetric SAR Images Based on the Integration of Improved Watershed and MRF Segmentation Approaches

Land cover and land use change detection is one of the most important applications of polarimetric synthetic aperture radar (PolSAR) data. In a pixel-based method, the speckle effects and the lack of consideration for spatial information produce false alarms. Therefore, an object-based image analysis has been used to overcome these weaknesses. This paper proposes a novel hybrid image segmentation algorithm for improving the accuracy of land cover change detection. This method consists of three steps. First, the segmentation of two PolSAR images by integrating segmentation techniques, namely, region-based improved watershed and an edge-based coupled Markov random field (MRF). Second, the selection of the optimal ratios of polarimetric features based on a genetic algorithm and Jefferies–Matusita distance criteria. Third, the binary classification of image objects using the ratio of mean pixel values of the corresponding image objects. Compared with the conventional watershed and multiresolution segmentation methods, the improved watershed reduces the speckle effects in PolSAR images and avoids the oversegmentation problem. The use of coupled MRF detects the most accurate edge positions and improves the hybrid segmentation approach. The proposed change detection method, as applied on unmanned aerial vehicle synthetic aperture full polarimetric images, demonstrates an overall accuracy of 90.14% and a kappa coefficient of 0.80. Compared with the pixel-based change detection method based on a support vector machine classifier for the selected subspace of ratios of polarimetric features, the proposed method improved the overall accuracy by 8.72% and the kappa coefficient by 0.18.

Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method

The 2015 Nepal earthquake and its aftershocks not only caused huge damage with severe loss of life and property, also induced many geohazards with the major type of landslide which should bring continuous threats to the affected region. To gain a better understanding of the landslides induced by this earthquake, we proposed a pixel-based change detection method for postearthquake landslide mapping by using bitemporal Landsat-8 remote sensing data [May 29, 2014 (pre-earthquake) and June 1, 2015 (postearthquake)]. Two river valleys (Trishuli river valley and Sun Koshi river valley) that contain important economic arteries linking Nepal and China were selected as the study areas. Validation of the mapping results with postearthquake high-resolution images from Google Earth shows that the pixel-based landslide mapping method is able to identify landslides with relatively high accuracy, and it also approves the applicability of Landsat-8 satellite for landslide mapping with its multispectral information. The spatial distribution analysis indicates that both river valleys are substantially affected by landslides, and the situation is even more serious in the high mountain areas. Landslides are generally found in areas of high elevation and large surface slopes, with mean values above 1600 m and 30°, respectively. These findings suggest that these areas suffer greatly from these geohazards, and the threat will continue for the next few years.

A supervised method for object-based 3D building change detection on aerial stereo images

Abstract. There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences. The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas. The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods (i.e. change vector analysis and principal component analysis-based method). In particular, the proposed sub-pixel change detection approach not only provides the binary change information, but also obtains the characterization about change direction and intensity, which greatly extends the semantic meaning of the detected change targets.

Assessment of the image misregistration effects on object-based change detection

High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ‘real’ changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ‘change’ objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used.

OBJECT-BASED CHANGE DETECTION USING GEOREFERENCED UAV IMAGES

Abstract. Unmanned aerial vehicles (UAV) have been widely used to capture and down-link real-time videos/images. However, their role as a low-cost airborne platform for capturing high-resolution, geo-referenced still imagery has not been fully utilized. The images obtained from UAV are advantageous over remote sensing images as they can be obtained at a low cost and potentially no risk to human life. However, these images are distorted due to the noise generated by the rotary wings which limits the usefulness of such images. One potential application of such images is to detect changes between the images of the same area which are collected over time. Change detection is of widespread interest due to a large number of applications, including surveillance and civil infrastructure. Although UAVs can provide images with high resolution in a portable and easy way, such images only cover small parts of the entire field of interest and are often with high deformation. Until now, there is not much application of change detection for UAV images. Also the traditional pixel-based change detection method does not give satisfactory results for such images. In this paper, we have proposed a novel object-based method for change detection using UAV images which can overcome the effect of deformation and can fully utilize the high resolution capability of UAV images. The developed method can be divided into five main blocks: pre-processing, image matching, image segmentation and feature extraction, change detection and accuracy evaluation. The pre-processing step is further divided into two sub-steps: the first sub-step is to geometrically correct the bi-temporal image based on the geo-reference information (GPS/INS) installed on the UAV system, and the second sub-step is the radiometric normalization using a histogram method. The image matching block uses the well-known scale-invariant feature transform (SIFT) algorithm to match the same areas in the images and then resample them. The image segmentation and feature extraction block is used to separate the images to different meaningful regions by the mean shift method, extract the textural features and contextual features (polygon,etc.) , Based on the features extracted above as well as the SIFT features of the area, the optimization result is achieved by considering the neighbourhood information .The proposed method is being tested by using multi-temporal images acquired by UAV. The results confirm the effectiveness of the proposed approach.

Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.