A method based on tree-structured Markov random field for forest area classification

Abstract

The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving.In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process.The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.

Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine

“…To provide a better and more effective image classification technique, it is important and urgent to study and develop new object-oriented image classification methods that incorporate both raster analysis and vector analysis. Recently, some researchers have succeeded in classification with new object-oriented classification methods adopting Markov random field (MRF) segmentation techniques and maximum likelihood classification (MLC) algorithm (Cuozzo et al 2004). Bruzzone (2004) proposed a multi-level hierarchical approach to classify high spatial resolution images with support vector machine (SVM), and the results and comparisons confirmed the effectiveness of the approach.…”

A method based on tree-structured Markov random field and a texture energy function for classification of remote sensing images

“…The differences and advantages between a TS-MRF and a flat MRF model is that: 1) TS-MRF uses a binary tree structure for carry out the classification, 2) in each tree node is estimated a binary MRF and all its associated parameters, 3) is a model recursive with fast optimization and 4) capable to split IEEE Catalog Number: CFP08827-CDR ISBN: 978-1-4244-2499-3 Library of Congress: 2008903800 978-1-4244-2499-3/08/$25.00 ©2008 IEEE highly correlated classes. Several works [9], [2], [10], [11] have verified that to use TS-MRF for land cover classification improves the classification accuracy and therefore interpretation.…”

Precise Crop Classification of Hyperspectral Images Using Multi-Branch Feature Fusion and Dilation-Based MLP

“…Many methods have been applied to hyperspectral image classification in recent years. Early-stage classification methods are support vector machine (SVM) [7], random forest (RF) [8], multiple logistic regression [9] and decision tree [10], which can provide promising classification results. What is more, these classification methods can only extract the shallow feature information of hyperspectral images, which have limited ability to handle the highly nonlinear HSI data and limit the further improvement of their classification accuracy.…”