Generalized Differential Morphological Profiles for Remote Sensing Image Classification

Huang, Xin; Han, Xiao-Jing; Zhang, Liangpei; Gong, Jianya; Liao, Wenzhi; Benediktsson, Jón Atli

doi:10.1109/jstars.2016.2524586

Cited by 42 publications

(16 citation statements)

References 34 publications

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“…Huang, Lu, and Zhang (2014) has proposed multiindex learning (MIL) method for HRS images using set of indices, such as, MBI, MSI, and Normalized Difference Vegetation Index (NDVI) to improve classification results over urban areas. Similarly, in order to improve classification accuracy using remote sensing image, Huang et al (2016) introduced Generalized Differential Morphological Profile (GDMP), which has been found advantageous over traditional Differential Morphological Profile (DMP) [Pesaresi and Benediktsson (2001)]. Further, Benediktsson, Pesaresi, & Amason (2003) investigated methods to pre-process the DMPs, such as, decision boundary feature extraction for neural networks, discriminant analysis feature extraction, and simple sorting feature selection in order to reduce the computational load when DMPs have been used for classification by neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

Gavankar

Ghosh

2017

European Journal of Remote Sensing

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Automatic building extraction from High-Resolution Satellite (HRS) image has been an important field of research in the area of remote sensing. Different techniques related to radiometric, geometric, edge detection and object based have already been discussed and used by various researchers for building extraction. However, faithfulness of extraction is highly dependent on user intervention. This study proposes a novel morphological based automatic approach for extraction of buildings using HRS image. Moreover, using such an automatic approach, buildings can be detected having different size and shape. The proposed technique integrates morphological Top-hat filter, and K-means algorithm to extract buildings having bright and dark rooftops. Further, extracted bright and dark rooftop building segments have been combined together to obtain the final output that contains final extracted building segments. In order to eliminate false-detected buildings, different parameters like area, eccentricity, and axis ratio (major/minor axis) have been used. The suitability of the technique has been judged using different indicators such as completeness, correctness and quality.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

Gavankar

Ghosh

2017

European Journal of Remote Sensing

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“…It has been acknowledged that DMP can utilize more spatial information in HRRSI than MP. Later, by utilizing features from the difference of one image to all other images in the profile, generalized differential morphological profile (GDMP) [53] is studied and a better classification performance has been reported on several standard datasets compared with DMP. The relationship of MP, DMP and GDMP is demonstrated in Figure 2.…”

Section: Morphological Profilementioning

confidence: 99%

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

Qin

Zhang

et al. 2019

Remote Sensing

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Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

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“…Notably, the processing method for a sub-image block is not limited to a median filter. In general, a spatial feature extraction approach, such as a morphology profile (MP) [41,42], can also be used. MP is an effective and classical approach for extracting the spatial information of a VHR image; morphological opening and closing operators are used in order to isolate bright (opening) and dark (closing) structures in the images.…”

Section: Sub-image Block Processing With Image Filter or Feature Extrmentioning

confidence: 99%

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

Lv¹,

Benediktsson

et al. 2016

Remote Sensing

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Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene scale decomposition (OSSD)-based system is proposed for the classification of VHR images. Different from the traditional methods, the OSSD-based system aims to improve the classification performance by decomposing the complexity of an image's content. First, an image scene is divided into sub-image blocks through segmentation to decompose the image content. Subsequently, each sub-image block is classified respectively, or each block is processed firstly through an image filter or spectral-spatial feature extraction method, and then each processed segment is taken as the feature input of a classifier. Finally, classified sub-maps are fused together for accuracy evaluation. The effectiveness of our proposed approach was investigated through experiments performed on different images with different supervised classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes classifier, and maximum likelihood classifier. Compared with the accuracy achieved without OSSD processing, the accuracy of each classifier improved significantly, and our proposed approach shows outstanding performance in terms of classification accuracy.

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Generalized Differential Morphological Profiles for Remote Sensing Image Classification

Cited by 42 publications

References 34 publications

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

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