Local optimal scale in a hierarchical segmentation method for satellite images

Abstract

Over recent decades, remote sensing has emerged as an effective tool for improv ing agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multiresolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchi cal segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intravariability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a sin gle image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.This work has been funded by the Centro de Recursos Hídricos para la Agricultura y la Minería (CONICYT/FONDAP/1513001).

Damaged region filling and evaluation by symmetrical exemplar-based image inpainting for Thangka

“…The damaged image region preprocessing and segmentation are complete following related research [24,25]. Several reference and research publications are available [26,27], yet this is not addressed herein. In order to achieve a clear and consecutive narrative, only some simple methods are given for image preprocessing and damaged region segmentation.…”

BSLIC: SLIC Superpixels Based on Boundary Term

“…Therefore, during the experiments, k takes its value at intervals of 100 from 50 to 2500. Weighting factor m is within the range of [5,15,30]. Figure 12 displays part of the experimental results.…”

“…Table 1 is the data of BR with σ = 1. The generated number of superpixels k is set in the range of [500, 1000, 1500, 2000, 2500], and m is [5,15,30]. For boundary recall, Table 1 shows that BSLIC can achieve at most a 6.23%, 8.56% and 12.43% increase when m is 5, 15 and 30.…”

“…Superpixels can capture image redundancy and transform pixel-level computing into a region-level operation [3], which can greatly reduce the complexity of subsequent image processing tasks. Superpixel segmentation has become an important pre-processing step in various image processing applications, such as image segmentation [4][5][6][7][8][9], saliency detection [10][11][12] and classification [13,14].…”

Deep learning for superpixel-based classification of remote sensing images

“…In this work, a modified version of SLIC (Gonzalo-Martín et al, 2016;Garcia-Pedrero et al, 2015) is used to generate SPs. This version extends the definition of spectral proximity provided by the original SLIC to work with multispectral images of B bands.…”