Taking optimal advantage of fine spatial information: promoting partial image reconstruction for the morphological analysis of very-high-resolution images

Diverse sensor technologies have allowed us to measure different aspects of objects on Earth's surface [such as spectral characteristics in hyperspectral images and height in light detection and ranging (LiDAR) data] with increasing spectral and spatial resolutions. Remote-sensing images of very high geometrical resolution can provide a precise and detailed representation of the monitored scene. Thus, the spatial information is fundamental for many applications. Morphological profiles (MPs) and attribute profiles (APs) have been widely used to model the spatial information of very-high-resolution (VHR) remote-sensing images. MPs are obtained by computing a sequence of morphological operators based on geodesic reconstruction. However, both morphological operators based on geodesic reconstruction and attribute filters (AFs) are connected filters and, hence, suffer the problem of leakage (i.e., regions related to different structures in the image that happen to be connected by spurious links are considered as a single object). Objects expected to disappear at a given stage remain present when they connect with other objects in the image. Consequently, the attributes of small objects are mixed with their larger connected objects, leading to poor performances on postapplications (e.g., classification).