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

Abstract

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...

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

“…In the proposed CD framework, the co-segmentation using the fractal net evolution approach (FNEA) is applied directly to the stacked bitemporal images to create spatially corresponding objects. FNEA is a region growing algorithm based on a minimum heterogeneity criteria and builds a multi-scale hierarchical structure by merging the neighboring image objects [57,58]. The segment parameters for the FNEA-based segmentation are adjusted and determined with the aid of the ESP tool in this paper.…”

“…In the experiments, five unsupervised CD methods are selected as the comparison algorithms to verify the advantages of the proposed CD approach, including the classic PCA-K-Means method [17], multi-scale superpixel and deep neural networks (MSDNN) for CD [61], region-based level set evolution (RLSE) method [26], multi-scale object histogram distance (MOHD) method [57], and the object-based unsupervised CD based on the SVM method, denoted as object-based SVM (OSVM) [25].…”

Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery

“…Using multi-source data, the Special Issue includes papers focusing on deep learning [3][4][5], multi-angle image processing [6], multi-source image fusion in heterogeneous environments [2,3,5], and object-based image analysis [5,7,8]. These interesting techniques applied various change detection applications, while most of them simultaneously mined the change information from the spectral, spatial, and temporal domains.…”

“…The object-based automatic land-cover change detection proposed by Lv et al [8] followed the unsupervised paradigm of bi-temporal feature difference. In this work, an object-level bin-to-bin histogram distance was adapted to measure the change magnitude between the pairwise objects of the bi-temporal images, which presented superiority to the current unsupervised spectral-spatial change detection techniques.…”

Sonar Image Detection Based on Multi-Scale Multi-Column Convolution Neural Networks

“…Image processing methods based on histograms are also widely used in the field of object detection. Lv et al [23] proposed a novel multi-scale object histogram distance (MOHD) to detect the target region of the remote sensing image. The method first calculates the frequency histogram of the image, then uses the bin-to-bin distance to measure the target change, and uses the Otsu algorithm to complete the target area segmentation.…”