A Biologically Inspired Object Spectral-Texture Descriptor and Its Application to Vegetation Classification in Power-Line Corridors

Abstract

The use of appropriate features to represent an output class or object is critical for all classification problems. In this letter, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of images or objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSFs) of a pulse-coupled neural network, which is invariant to rotation, translation, and small scale changes. The proposed method is first evaluated in a rotation- and scale-invariant texture classification using the University of Southern California Signal and Image Processing Institute texture database. It is further evaluated in an application of vegetation species classification in power-line corridor monitoring using airborne multispectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective in representing the spectral-texture patterns of objects, and it shows better results than classic color histogram and texture features.