A new technique for the segmentation of single- and multiresolution (MR) remote sensing images is proposed. To guarantee the preservation of details at fine scales, edge-based watershed is used, with automatically generated markers that help in limiting oversegmentation. For MR images, the panchromatic and multispectral components are processed independently, extracting both the edge maps and the morphological and spectral markers that are eventually fused at the highest resolution, thus avoiding any information loss induced by pansharpening. Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.

Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images

This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data. This article also proposes to apply the principal component (PC) technique to the multipolarized synthetic aperture radar (SAR), prior to the optical and radar data PC fusion process, aiming at the use of all available polarized information in the fusion process. Although the fused images improve the visual interpretation of the land use classes, the best results are achieved with the simple combination of the Advanced Land Observing Satellite (ALOS)/phased array L-Band SAR (PALSAR) with the LANDSAT5/Thematic Mapper (TM) images. Radar information is found to be particularly useful for improving the user accuracies (UAs) of Soybean with 40 days af...

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Image segmentation is a key step in geographic object-based image analysis. Numerous segmentation techniques, e.g., watershed segmentation, mean-shift segmentation, and fractal net evolution algorithm, have been proposed and applied for various types of image analysis tasks. The majority of the segmentation algorithms require a user-defined parameter, namely, the scale parameter, to control the sizes of segments, yet the automation of the scale parameter remains a great challenge. Over the past few years, several automated parameterization methods, such as the estimation of scale parameters (ESP) tool, have been developed. However, few of the existing methods are able to enhance both intrasegment homogeneity and intersegment heterogeneity. In this letter, we proposed an energy function method that aimed at enhancing the characteristics of intrasegment homogeneity and intersegment heterogeneity, simultaneously, to identify the optimal segmentation scale for image segmentation. The intersegment heterogeneity was calculated as the weighted gradient from a segment to its neighbors by spectral angle, whereas the intrasegment homogeneity was quantified by the mean spectral angle within a segment. The performance of the proposed method was evaluated by applying it to a WorldView-2 multispectral image of Toronto, Canada, and comparing it with the local-peak-based method, which considered only the intrasegment homogeneity of an image. The scale parameter identified by the proposed method can better characterize the reference geo-objects over the entire image. The accuracy assessment result shows that the proposed method outperformed the existing technique by reducing the discrepancy by 17.9%.

An Automated Method to Parameterize Segmentation Scale by Enhancing Intrasegment Homogeneity and Intersegment Heterogeneity

In this paper, a region segmentation technique for remote sensing images using a boosted committee of Convolutional Neural Networks (CNNs) coupled with inter-band and intra-band fusion, is proposed. The vast heterogeneity in remote sensing images restricts the application of existing segmentation methods that often rely on a set of predefined feature detectors along with tunable parameters. Therefore, it is highly challenging to design a segmentation technique which could achieve high accuracy while simultaneously maintaining strong generalization particularly for visual data with improved spatial, spectral, and temporal resolutions. The proposed method is a fusion framework consisting of a set of thirty boosted networks that derive individual probability maps on the location of region boundaries from the different multi-spectral bands and combines them into one using an averaging inter-band fusion scheme. The boundaries are then thinned, connected, and region segmented using a morphological intra-band fusion scheme. Qualitative and quantitative results, on publicly-available datasets, confirm the superiority of the proposed segmentation method over existing state-of-art techniques. In addition, the paper also demonstrates the effect of some variations in design-choices of the proposed method.

Supervised remote sensing image segmentation using boosted convolutional neural networks

Remotely sensed data are widely used to identify, delineate, and characterize wetlands. Optical data provide interesting information to inventory vegetation and agricultural practices in wetlands but are limited to cloud-free periods. For this reason it is not possible to precisely delineate the extent of saturated areas as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time-series datasets to identify and locate the seasonal dynamics of saturated areas in wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multitemporal analysis of wetland evolution and polarimetric decomposition method is presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results were validated using grou...

One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images

A digital image can be processed by an image processing method that calculates a gradient map for the digital image, calculates a density function for the gradient map, calculates a modified gradient map using the gradient map, the density function and the selected scale level, and segments the modified gradient map. Prior to segmenting the modified gradient map, a sub-image of the digital image can be segmented at the selected scale level to determine if the selected scale level will give the desired segmentation.

Jin Xiaoying

Papers

Segmentation-based image processing system