FAST MEAN-SHIFT BASED CLASSIFICATION OF VERY HIGH RESOLUTION IMAGES: APPLICATION TO FOREST COVER MAPPING
Keywords: Classification, Forestry, High resolution, Imagery, Mapping, Performance
Abstract. This paper presents a new unsupervised classification method which aims to effectively and efficiently map remote sensing data. The Mean-Shift (MS) algorithm, a non parametric density-based clustering technique, is at the core of our method. This powerful clustering algorithm has been successfully used for both the classification and the segmentation of gray scale and color images during the last decade. However, very little work has been reported regarding the performance of this technique on remotely sensed images. The main disadvantage of the MS algorithm lies on its high computational costs. Indeed, it is based on an optimization procedure to determine the modes of the pixels density. To investigate the MS algorithm in the difficult context of very high resolution remote sensing imagery, we use a fast version of this algorithm which has been recently proposed, namely the Path-Assigned Mean Shift (PAMS). This algorithm is up to 5 times faster than other fast MS algorithms while inducing a low loss in quality compared to the original MS version. To compensate for this loss, we propose to use the K modes (cluster centroids) obtained after convergence of the PAMS algorithm as an initialization of a K-means clustering algorithm. The latter converges very quickly to a refined solution to the underlying clustering problem. Furthermore, it does not suffer the main drawback of the classic K-means algorithm (the number of clusters K needs to be specified) as K is automatically determined via the MS mode-seeking procedure. We demonstrate the effectiveness of this two-stage clustering method in performing automatic classification of aerial forest images. Both individual bands and band combination trails are presented. When compared to the classical PAMS algorithm, our technique is better in terms of classification quality. The improvement in classification is significant both visually and statistically. The whole classification process is performed in a few seconds on image tiles of around 1000 x 1000 pixels making this technique a viable alternative to traditional classifiers.