Attribute Profiles on Derived Textural Features for Highly Textured Optical Image Classification
Morphological attribute profiles (APs) have thus far been proven effective for remote sensing image classification by several research studies. However, recent studies have shown that a direct application of APs to highly textured and structured images, especially in very high-resolution (VHR) optical imagery, may be insufficient. Some solutions have been proposed to deal with this issue, such as to extract the local histograms and the local features of AP images [histogram-based APs (HAPs) and local feature-based APs (LFAP), respectively], or to combine APs with different textural features. In this letter, we review these approaches and then propose a novel strategy, which directly generates APs on some derived textural features instead of separately combining them. Experimental results from both natural textures and VHR optical remotely sensed images show that the proposed approach can produce superior classification performance than the standard APs, HAPs, LFAPs, as well as the classical combination of APs with textural features.