Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique

Fu, Gang; Zhao, Huijuan; Li, Cong; Shi, Larry

doi:10.3390/rs5073259

Cited by 34 publications

(14 citation statements)

References 18 publications

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“…Most of the object-oriented approaches perform a "segmentation-classification" mode. In the segmentation stage, Multi-Resolution (MR) [11], Full-Lambda Schedule (FLS) [12], Mean-Shift [13], Quadtree-Seg [14], and other image segmentation approaches are used to generate image segments, which we called image objects. In the classification stage, object features (color, texture, and geometric features) are calculated, which are taken as inputs of supervised or unsupervised classification, or a manually designed rule set for feature filtering, to achieve the final class discrimination.…”

Section: Introductionmentioning

confidence: 99%

Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

et al. 2017

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Abstract:As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.

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Section: Introductionmentioning

confidence: 99%

Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

et al. 2017

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“…In Fu et al 16 it was used on spatial context, overcoming the difficulties about image splitting by adding the spatial indexing mechanism based on improved Morton coding; the problems with region merging was treated by a process based on region adjacency graph (RAG). The method was validated on GeoEye-1 and IKONOS colour images, and the results showed that the efficiency was considerably increased, as well as the accuracy, when compared with another typical algorithms.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy segmentation tool for remote sensing data

2014

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Remote sensing data are an important source of information for a variety of applications, such as coastal mapping applications, monitor land use, and chart wildlife habitats, for example. One of the most important task for these data analysis is the segmentation. Segmentation means the action of merging neighbouring pixels into segments (or regions), based on their homogeneity or heterogeneity parameters. Traditional image segmentation methods looks for delineating discrete image objects with sharp edges, which cannot be always possible, mainly considering that many geographic objects, both natural and man-made, may not appear clearly bounded in remotely sensed images. A fuzzy approach seems natural in order to capture the structure of objects in the image and takes into account the fuzziness of the real world and the ambiguity of remote sensing imagery. The main goal of this work is define boundaries of objects in an image. This proposal aims to be faster than other segmentation approaches inside the TerraLib tools by considering only the neighbourhood of a selected pixel. This work proposes the use of image's tone and colour to select and define objects in remote scenes based on fuzzy rules. The fuzzy set is defined by an input tolerance level, which can be adjustable according to the desired granularity of the selection. The proposal methodology is not limited by the selection of only one object, that is, the mask can be designed by a set of objects with different features and tolerances. The algorithm also returns the objects size and proportion. The quality of the individual segmentation results is evaluated based on multi-spectral Landsat 5-TM, Landsat 7-ETM+ and CBERS data. This is done by visual comparison, which is supplemented by a detailed investigation using visual interpreted reference areas.

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“…Remote sensing can provide prompt and accurate information on earth observations and also give valuable and resourceful information of deforestation for decisionmakers of forest fire management system and forest analysts to take essential actions as it provides more information especially in high altitude problem areas such as of landscape change, monitoring forest degradation and deforestation, sea water level calculations, etc [Fu et al, 2013]. Hence the paper focuses on improving the remote sensing image segmentation using texture feature extraction algorithm for analyzing 170 deforestation.…”

Section: Introductionmentioning

confidence: 99%

Improving segmentation accuracy for detecting deforestation using texture feature derived from Landsat 8 OLI multispectral imagery

Menaka¹,

Singh

2015

European Journal of Remote Sensing

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For the identification of deforestation, numerous techniques and features have been developed and used to maximize the segmentation accuracy. Choosing appropriate texture features and extraction methods are one of the most significant issues in remote sensing image applications. This work proposes a Modified Sum of Difference using diagonal texture feature extraction with multiresolution segmentation algorithm to maximize the segmentation accuracy. First, the Modified Sum of Difference using diagonal matrix (MSODDM) algorithm is applied to the input image; this divides the image into a number of coarse objects. Secondly, the multiresolution algorithm with the threshold value of 200 is applied to the coarse image produced by MSODDM for getting finer segmentation result. Finally, the accuracy of the segmented image is compared with the sample input image used in the experimental study. Thus, the proposed method generates more promising result of reaching 97.02% of segmentation accuracy, which results in better classification of remote sensing images in detecting deforestation.

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Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique

Cited by 34 publications

References 18 publications

Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

A fuzzy segmentation tool for remote sensing data

Improving segmentation accuracy for detecting deforestation using texture feature derived from Landsat 8 OLI multispectral imagery

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