The new generation of hyperspectral sensors can provide images with a high spectral and spatial resolution. Recent improvements in mathematical morphology have developed new techniques such as the Attribute Profiles (APs) and the Extended Attribute Profiles (EAPs) that can effectively model the spatial information in remote sensing images. The main drawbacks of these techniques is the selection of the optimal range of values related to the family of criteria adopted to each filter step, and the high dimensionality of the profiles, which results in a very large number of features and therefore provoking the Hughes phenomenon. In this work, we focus on addressing the dimensionality issue, which leads to an highly intrinsic information redundancy, proposing a novel strategy for extracting spatial information from hyperspectral images based on the analysis of the Differential Attribute Profiles (DAPs). A DAP is generated by computing the derivative of the AP; it shows at each level the residual between two adjacent levels of the AP. By analyzing the multilevel behavior of the DAP, it is possible to extract geometrical features corresponding to the structures within the scene at different scales. Our proposed approach consists of two steps: 1) a homogeneity measurement is used to identify the level L in which a given pixel belongs to a region with a physical meaning; 2) the geometrical information of the extracted regions is fused into a single map considering their level L previously identified. The process is repeated for different attributes building a reduced EAP, whose dimensionality is much lower with respect to the original EAP ones. Experiments carried out on the hyperspectral data set of Pavia University area show the effectiveness of the proposed method in extracting spatial features related to the physical structures presented in the scene, achieving higher classification accuracy with respect to the ones reported in the state-of-the-art literature
The analysis of changes occurred in multi-temporal images acquired by the same sensor on the same geographical
area at different dates is usually done by performing a comparison of the two images after co-registration. When
one considers very high resolution (VHR) remote sensing images, the spatial information of the pixels becomes
very important and should be included in the analysis. However, taking into account spatial features for change
detection in VHR images is far from being straightforward, due to effects such as seasonal variations, differences
in illumination condition, residual mis-registration, different acquisition angles, etc., which make the comparison
of the structures in the scene complex to achieve from a spatial perspective. In this paper we propose a change
detection technique based on morphological Attribute Profiles (APs) suitable for the analysis of VHR images.
In greater detail, this work aims at detecting the changes occurred on the ground between the two acquisitions
by comparing the APs computed on the image of each date. The experimental analysis has been carried out on
two VHR multi-temporal images acquired by the Quickbird sensor on the city of Bam, Iran, before and after
the earthquake occurred on Dec. 26, 2003. The experiments confirm that the APs computed at different dates
show different behaviors for changed and unchanged areas. The change detection maps obtained by the proposed
technique are able to detect changes in the morphology of the correspondent regions at different dates regardless
their spectral variations.