Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. In recent years, it has aroused widespread interest due to the explosive development of artificial intelligence (AI) technology, and change detection algorithms based on deep learning frameworks have made it possible to detect more delicate changes (such as the alteration of small buildings) with the help of huge amounts of remote sensing data, especially high-resolution (HR) data. Although there are many methods, we still lack a deep review of the recent progress concerning the latest deep learning methods in change detection. To this end, the main purpose of this paper is to provide a review of the available deep learning-based change detection algorithms using HR remote sensing images. The paper first describes the change detection framework and classifies the methods from the perspective of the deep network architectures adopted. Then, we review the latest progress in the application of deep learning in various granularity structures for change detection. Further, the paper provides a summary of HR datasets derived from different sensors, along with information related to change detection, for the potential use of researchers. Simultaneously, representative evaluation metrics for this task are investigated. Finally, a conclusion of the challenges for change detection using HR remote sensing images, which must be dealt with in order to improve the model’s performance, is presented. In addition, we put forward promising directions for future research in this area.
Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.
Although optical remote sensing can capture the Earth's environment with visible and infra-red sensors, it is limited by weather conditions. Often, only a few sets of cloudfree optical imagery are available in cloudy regions, where many agricultural towns are located. On the other hand, radar remote sensing can capture imagery under cloudy conditions. In this study, we examined the capability of Sentinel-1 multitemporal dual-polarized SAR imagery in a whole year from Google Earth Engine in crop mapping in two study sites in Chongqing, China, and Landivisiau, France. Results show that it is possible to produce better crop classification maps using multitemporal SAR imagery, but the performance is limited by local terrain. Flat agricultural regions, such as Western Europe, are expected to benefit from the multitemporal SAR information. Mountain agricultural regions, such as Southwestern China, will encounter difficulties due to the undulate terrain. We also tested two sampling strategies, i.e., random sampling and regional sampling, and observed high variation in overall accuracy: the former led to a higher accuracy. The gap is caused by the diversity of training sets examined using tSNE visualization. The importance of SAR channels in each month are correlated with their entropy. Data from the growing season are important in distinguishing crop types. 3D CNN achieved similar results under a huge computation cost compared with 2D CNNs. Based on the experiments, we recommend to use light-weight 2D CNN that can run on CPU for real-world crop mapping with SAR data.
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