I. Introduction
Land cover change detection (LCCD) with remotely sensed images (RSIs) is an important technology for observing Earth’s surface changes [1], [2], [3]. Owing to the advantages of remotely sensed technology in effectively capturing large-scale land cover [4], [5], [6], LCCD with RSIs has been widely used in land-use motoring [7], geological disaster detection [8], [9], natural resource management [10], and environmental monitoring [11]. However, LCCD usually refers to more than two or three temporal RSIs in a detection task, and some challenges are usually encountered in practical applications, as presented below.
Spectral noise is inevitable in an imaging process for RSIs [12]. The imaging progress depends on two aspects: the platform and the sensor. Intuitively, pre-processing operations, such as geometry and reflectance correction, are required for a raw RSI acquired with a remotely sensed platform (such as a satellite or an aerial plane). This operation may smoothen some noise or bring some noise to a processed image [13], [14]. Additionally, remote sensors belong to optical physical equipment, which produces some noise in the imaging process. Moreover, the noise from the equipment is inevitable.
Pseudo-change is observed as noise in a change detection map when using bitemporal RSIs [15]. The bitemporal RSIs used for LCCD cover the same geography but occur on different dates. External imaging conditions, such as atmospheric moisture, phenology difference, and sun height, cause pseudo-changes in an LCCD task [16], [17]. Additionally, mixing pixels may also occur in RSIs and result in accused pseudo-changes [18]. These pseudo-changes may be considered as noise in a detection map and reduce the detection accuracies.
Ground targets on the Earth’s surface with various shapes and sizes challenge the utilization of spatial-contextual features. Many studies have demonstrated that utilizing spatial-contextual features can improve the application performance with RSIs [19]. The ground targets in an image scene with different shapes and sizes result in changed and unchanged areas between bitemporal RSIs, also with varied shapes and sizes. Utilizing spatial-contextual features for LCCD with RSIs is a challenge.