Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Chen, Gang; Weng, Qihao; Hay, Geoffrey J.; He, Ying

doi:10.1080/15481603.2018.1426092

Cited by 234 publications

(155 citation statements)

References 113 publications

Supporting

Mentioning

127

Contrasting

Unclassified

Order By: Relevance

“…The building structure of FCN is shown in Figure 3, including the convolutional operation, regularization dropout method [50], Rectified Linear Unit (ReLU) activation function [51], summation operation [24], max pooling, and deconvolutional operation [35]. Deconvolutional operation is the key to implement the FCN and differentiate itself from the DCNN.…”

Section: Fully Convolutional Networkmentioning

confidence: 99%

“…When compared with other traditional classifiers, deep learning does not require feature engineering, which attracted many researchers from the remote sensing community to test its usability for landcover mapping [19][20][21][22][23]. Two latest review papers [20,24] on OBIA both also emphasize the need for testing deep learning techniques under the OBIA framework.Deep learning networks normally have a huge number of parameters to be adjusted during the training procedure and may require massive training samples to trigger its power, as shown in one of the latest studies [25], but collecting training samples is expensive for remote sensing applications. To overcome the scarce training samples limitation, several strategies have been proposed, such as augmenting the limited labeled samples with various transformation operations, such as rotation, translation and scaling [26,27], unsupervised pre-training [11,28], transfer learning [29,30], etc.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Liu

Abd-Elrahman

2018

Remote Sensing

View full text Add to dashboard Cite

Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data.Remote Sens. 2018, 10, 457 2 of 24 all the UAS images. Then, image segmentation algorithm is conducted to segment the orthoimage to groups of homogeneous pixels to form numerous meaningful objects. Spectral, geometrical, textural, and contextual features are extracted from these objects and used as input to different classifiers, such as Random Forest (RF) [9] and Support Vector Machine (SVM) [10], to label the objects. Feature extraction and selection that have to be conducted during traditional OBIA procedures are challenging tasks and can limit classification performance.Recently, the rise of deep learning techniques provided an alternative to traditional land cover classifiers. Deep learning brought about around 2006 [11], became well known in the computer vision community around 2012, since one supervised version of deep learning networks Deep Convolutional Neural Networks (DCNN) made a breakthrough for scene classification tasks [12,13], and has reached out to many industrial applications and other academic areas in recent years as it continues to advance technologies in areas, like speech recognition [14], medical diagnosis [15], autonomous driving [16], or even the gaming world [17,18]. When compared with other traditional classifiers, deep learning does not require feature e...

show abstract

Section: Fully Convolutional Networkmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Liu

Abd-Elrahman

2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…With the rapid development of high resolution remote sensing imaging techniques, geographic object-based image analysis (GEOBIA) has become a promising paradigm to extract accurate and reliable ground information from various detectors [1,2]. GEOBIA framework typically encompasses several sub-procedures such as image segmentation, geo-object recognition, feature extraction and image classification [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities

et al. 2019

View full text Add to dashboard Cite

Image segmentation is extensively used in remote sensing spectral image processing. Most of the existing region merging methods assess the heterogeneity or homogeneity using global or pre-defined parameters, which lack the flexibility to further improve the goodness-of-fit. Recently, the local spectral angle (SA) threshold was used to produce promising segmentation results. However, this method falls short of considering the inherent relationship between adjacent segments. In order to overcome this limitation, an adaptive SA thresholds methods, which combines the inter-segment and boundary homogeneities of adjacent segment pairs by their respective weights to refine predetermined SA threshold, is employed in a hybrid segmentation framework to enhance the image segmentation accuracy. The proposed method can effectively improve the segmentation accuracy with different kinds of reference objects compared to the conventional segmentation approaches based on the global SA and local SA thresholds. The results of the visual comparison also reveal that our method can match more accurately with reference polygons of varied sizes and types.

show abstract

“…As an important step towards scene understanding [5], segmentation plays a vital role in many important remote sensing applications [6], such as natural hazards detection [7], urban planning [8,9], land cover mapping [10] and so on. Unlike the classical paradigm in geographic object-based image analysis that unsupervised segmentation is followed by classification [11][12][13][14], semantic segmentation employs a pixel-level supervised style and assigns each pixel with a pre-designed label.…”

Section: Introductionmentioning

confidence: 99%

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

et al. 2018

View full text Add to dashboard Cite

Abstract:The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

show abstract

Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Cited by 234 publications

References 113 publications

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

Contact Info

Product

Resources

About