In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.

Accurately identifying inefficient urban land is essential for urban regeneration and mining underutilized assets. Previous studies have primarily focused on examining the overall efficiency of land use without adequately considering the heterogeneity of urban land use types and comprehensive characteristics of urban quality. As a result, the spatial accuracy and precision of research findings have been relatively low. To address this gap, we developed a comprehensive method to identify inefficient urban lands for residential, commercial, and industrial use. The method integrated multi-source geographic data to quantitatively characterize the efficiency of different land use types considering six key dimensions, including building attribute, urban service, transportation condition, environmental quality, business performance, and production efficiency, utilized principal component analysis to reduce the multicollinearity and the dimensionality of the data, and identified land clusters with similar features that were inefficiently used by means of hierarchical clustering. By applying the method to Changsha, China, we validated its effectiveness. The results demonstrate that the method can accurately identify inefficient residential, commercial, and industrial land, with kappa coefficients of 0.71, 0.77, and 0.68, respectively. The identification results reveal the spatial distribution patterns of different types of inefficient land. Inefficient residential land is concentrated towards the city center, particularly in central areas. Inefficient commercial land is relatively evenly distributed, mainly outside the core commercial regions. Inefficient industrial land clusters towards the periphery, forming several agglomeration areas centered around industrial parks. By precisely identifying inefficient urban land and focusing on the key influencing factors, the proposed method enables the site selection of urban regeneration, site redevelopment evaluation, and optimization of urban resources.

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking.

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.

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic $c$ -means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.