Novel shape indices for vector landscape pattern analysis

Zhang, Ce; Atkinson, Peter M.

doi:10.1080/13658816.2016.1179313

Cited by 13 publications

(6 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The object convolutional position analysis (OCPA) is employed based on the moment bounding (MB) box of each object to identify the position of LIW-CNN and those of SIW-CNNs. The MB box, proposed by Zhang and Atkinson, (2016), refers to the minimum bounding rectangle built upon the moment orientation (the orientation of the major axis) of a polygon (i.e. an object), derived from planar characteristics defined by mechanics (Zhang and Atkinson, 2016;Zhang et al, 2006).…”

Section: Object Convolutional Position Analysis (Ocpa)mentioning

confidence: 99%

“…The MB box, proposed by Zhang and Atkinson, (2016), refers to the minimum bounding rectangle built upon the moment orientation (the orientation of the major axis) of a polygon (i.e. an object), derived from planar characteristics defined by mechanics (Zhang and Atkinson, 2016;Zhang et al, 2006). The MB box theory is briefly described hereafter.…”

Section: Object Convolutional Position Analysis (Ocpa)mentioning

confidence: 99%

See 1 more Smart Citation

An object-based convolutional neural network (OCNN) for urban land use classification

Zhang

Sargent

Pan

et al. 2018

Remote Sensing of Environment

Self Cite

352

221

View full text Add to dashboard Cite

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

show abstract

Section: Object Convolutional Position Analysis (Ocpa)mentioning

confidence: 99%

Section: Object Convolutional Position Analysis (Ocpa)mentioning

confidence: 99%

An object-based convolutional neural network (OCNN) for urban land use classification

Zhang

Sargent

Pan

et al. 2018

Remote Sensing of Environment

Self Cite

352

221

View full text Add to dashboard Cite

show abstract

“…The OCNN is trained as for the standard CNN model with labelled image patches, whereas the model prediction labels each segmented object derived from image segmentation. For each image object (polygon), a minimum moment bounding box was constructed by anisotropy with major and minor axes (Zhang and Atkinson, 2016). The centre point intersected with the polygon and the bisector of the major axis was used to approximate the central location of each image patch, where the convolutional process is implemented once per object.…”

Section: Object-based Convolutional Neural Network (Ocnn)mentioning

confidence: 99%

Joint Deep Learning for land cover and land use classification

Zhang

Sargent

Pan

et al. 2019

Remote Sensing of Environment

Self Cite

345

163

View full text Add to dashboard Cite

Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. In this paper, for the first time, a highly novel joint deep learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neural network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representations. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration. The proposed JDL method provides a general framework within which the pixel-based MLP and the patch-based CNN provide mutually complementary information to each other, such that both are refined in the classification process through iteration. Given the well-known complexities associated with the classification of very fine spatial resolution (VFSR) imagery, the effectiveness of the proposed JDL was tested on aerial photography of two large urban and suburban areas in Great Britain (Southampton and Manchester). The JDL consistently demonstrated greatly increased accuracies with increasing iteration, not only for the LU classification, but for both 2 the LC and LU classifications, achieving by far the greatest accuracies for each at around 10 iterations. The average overall classification accuracies were 90.18% for LC and 87.92% for LU for the two study sites, far higher than the initial accuracies and consistently outperforming benchmark comparators (three each for LC and LU classification). This research, thus, represents the first attempt to unify the remote sensing classification of LC (state; what is there?) and LU (function; what is going on there?), where previously each had been considered separately only. It, thus, has the potential to transform the way that LC and LU classification is undertaken in future. Moreover, it paves the way to address effectively the complex tasks of classifying LC and LU from VFSR remotely sensed imagery via joint reinforcement, and in an automatic manner.

show abstract

“…The authors start with an initial non-semantic image segmentation using meanshift (Comaniciu and Meer, 2002), the resultant segments are then considered to correspond to objects for which land use is to be predicted. These segments are split into rectangular patches using the moment bounding box method of Zhang and Atkinson (2016). These patches, which consist of either 48 x 48 or 128 x 128 pixels, are classified independently from each other using a CNN.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Multi-Scale Hierarchical Land Use Classification for the Verification of Geospatial Databases

Chun

Rottensteiner

Heipke

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.

show abstract

Novel shape indices for vector landscape pattern analysis

Cited by 13 publications

References 55 publications

An object-based convolutional neural network (OCNN) for urban land use classification

An object-based convolutional neural network (OCNN) for urban land use classification

Joint Deep Learning for land cover and land use classification

CNN-Based Multi-Scale Hierarchical Land Use Classification for the Verification of Geospatial Databases

Contact Info

Product

Resources

About