In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context.

Effective spatial-spectral pixel description is of crucial significance for the classification of hyperspectral remote sensing images. Attribute profiles are considered as one of the most prominent approaches in this regard, since they can capture efficiently arbitrary geometric and spectral properties. Lately though, the advent of deep learning in its various forms has also led to remarkable classification performances by operating directly on hyperspectral input. In this letter, we explore the collaboration potential of these two powerful feature extraction approaches. Specifically, we propose a new strategy for hyperspectral image classification, where attribute filtered images are stacked and provided as input to convolutional neural networks. Our experiments with two real hyperspectral remote sensing data sets show that the proposed strategy leads to a performance improvement, as opposed to using each of the involved approaches individually.