Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images

International audienceA Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing parameter. For this purpose, we introduce the new concepts of dynamic blocks and class label co-occurrence matrices. The estimation is then based on the analysis of the balance of spatial and spectral energies computed using the spatial class co-occurrence distribution and dynamic blocks. Moreover, we construct a new spatially weighted parameter to preserve the edges, based on the Canny edge detector. We evaluate the performance of the proposed method on three data sets: a multispectral DigitalGlobe WorldView-2 and two hyperspectral images, recorded by the AVIRIS and the ROSIS sensors, respectively. The experimental results show that the proposed method succeeds in estimating the optimal smoothing parameter and yields higher classification accuracies when compared to the state-of-the-art methods

Smoothing parameter estimation for Markov random field classification of non-Gaussian distribution image

International audienceIn the context of remote sensing image classification, Markov random fields (MRFs) have been used to combine both spectral and contextual information. The MRFs use a smoothing parameter to balance the contribution of the spectral versus spatial energies, which is often defined empirically. This paper proposes a framework to estimate the smoothing parameter using the probability estimates from support vector machines and the spatial class co-occurrence distribution. Furthermore, we construct a spatially weighted parameter to preserve the edges by using seven different edge detectors. The performance of the proposed methods is evaluated on two hyperspectral datasets recorded by the AVIRIS and ROSIS and a simulated ALOS PALSAR image. The experimental results demonstrated that the estimated smoothing parameter is optimal and produces a classified map with high accuracy. Moreover, we found that the Canny-based edge probability map preserved the contours better than others

Fully spatially adaptive smoothing parameter estimation for Markov random field super-resolution mapping of remotely sensed images

International audienceThis article presents a fully spatially adaptive Markov random field (MRF)-based super-resolution mapping (SRM) technique to produce land-cover maps at a finer spatial resolution than the original coarse-resolution image. MRF combines the spectral and spatial energies; hence, an MRF-SRM technique requires a smoothing parameter to manage the contributions of these energies. The main aim of this article is to introduce a new method called fully spatially adaptive MRF-SRM to automatically determine the smoothing parameter, overcoming limitations of the previously proposed approaches. This method estimates the number of endmembers in each image and uses them to assess the proportions of classes within each coarse pixel by a linear spectral unmixing method. Then, the real pixel intensity vectors and the local properties of each coarse pixel are used to compute the local spectral energy change matrix and the local spatial energy change matrix for each coarse pixel. Each pair of matrices represents all possible situations in spatial and spectral energy change for each coarse pixel and can be used to examine the balance between spatial and spectral energies, and hence to estimate a smoothing parameter for each coarse pixel. Thus, the estimated smoothing parameter is fully spatially adaptive with respect to real pixel spectral vectors and their local properties. The performance of this method is evaluated using two synthetic images and an EO1-ALI (The Advanced Land Imager instrument on Earth Observing-1 satellite) multispectral remotely sensed image. Our experiments show that the proposed method outperforms the state-of-the-art techniques

Markov random field models for classification of remote sensing data and super-resolution mapping

The most important issues in optimization based computer vision problems are the representation of the hidden quantities amongst feature classes, and defining an objective function, both items of which are interrelated. The hidden quantities can be represented through a discrete set of labels which represent the pixel intensities. An objective function also known as the energy function can encode all contextual constraints between the image pixels using the Markov random field, which has its roots in probability theory and graphical modelling. The energy function in MRF consists of two terms, the data or spectral energy term and an a priori term known as spatial energy which encodes the contextual constraints. This thesis proposes two smoothing parameter estimation techniques for data under the assumptions of Gaussian and non-Gaussian statistical distributions based on the balance of spatial and spectral energies concept. As well, this thesis constructs a new spatial energy component to preserve the edge using a new spatially weighted parameter and also a new index called edge probability map which employs the Canny edge detector. This thesis has also employed the WMM as data term to develop two novel techniques for WMM-MRF and WMM-HMRF for multi-look PolSAR data. The smoothing parameters of these methods are estimated using a combination of the aforementioned techniques, and the edge probability maps are produced using Gaussian–Gamma-shaped (GGS) bi-window method . Among the existing methods of super-resolution mapping (SRM), the family of MRF-SRM uses both spectral and contextual information to find the optimum position for each sub-pixel. A critical parameter in MRF-SRM is the smoothing parameters. This thesis introduces a fully spatially adaptive MRF based super-resolution mapping (SRM) technique which can automatically determine the smoothing parameter using both local spectral and local contextual information which are based on the pixel intensity vectors, and the local properties. All developed methods have been tested using real and synthetic optical and SAR images to demonstrate that they yield higher classification accuracies when compared to the state-of-the-art methods

Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region

Abstract Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species’ potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams