Fusion of Hyperspectral and VHR Multispectral Image Classifications in Urban Α–areas

Abstract

ABSTRACT:An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification.

A Two-Step Decision Fusion Strategy: Application to Hyperspectral and Multispectral Images for Urban Classification

“…The model integrates contrast information (Hervieu et al, 2016) that is included in the MS image and verifying:…”

“…The energy term is composed of a data term and a regularization term , the model from Hervieu et al (2016) was adapted to deal only with classification rectification instead of fusion. It uses a graphical model, where the energy model is a probabilistic function of the posterior probability .…”

“…SVM's, majority voting, and absolute maximum rule). Hervieu et al (2016) combined the classification results of HS and MS images, within a graphical energetic CRF model optimized using a graph-cut algorithm. This paper proposes a fusion scheme at the decision level to merge a low resolution LR-HS and a VHR-MS images.…”

Mapping urban impervious surfaces from an airborne hyperspectral imagery using the object-oriented classification approach

“…At the same time, the recent developments in hyperspectral domain have stressed on the need for advanced classification methods [12]. Recently, [13] proposed a generic method for fusion of hyperspectral and VHR multispectral image classifications in urban areas. The proposed fusion model relies on posterior class probabilities which are integrated through an energy minimization process [13].…”

“…Recently, [13] proposed a generic method for fusion of hyperspectral and VHR multispectral image classifications in urban areas. The proposed fusion model relies on posterior class probabilities which are integrated through an energy minimization process [13]. Furthermore, [12] have proposed a new methodology for spectral-spatial classification of hyperspectral images.…”