Optimizing the Selection of Spatial and Non-spatial Data for Higher Accuracy Multi-scale Classification of Urban Environments

Due to the broadly application of remote sensing imagery, there is an eager need for the classification of objects in the images. The multi-scale classification based on object oriented analysis is not a usual approach for image classification because the users of multi-scale classification do not know how to use the information from multiple scales to do multi-scale classification. Many users rely on some easily accessible tools. nearest neighbour classifier, to do multi-scale classification. The multi-scale classification classifies the images from different scales. The feature values of the object vary from different scales and they may have some trends against scales. These trends may help us to understand multi-scale classification better. This is the scale dependency of features. The difference between multi-scale classification and single-scale classification is not only multiple scales, but also the use of information from different scales. In order to explore the connection between different scales, the research of new features is necessary.

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A multi-scale classification of vegetation dynamics in arid lands: What is the right scale for models, monitoring, and restoration?

Journal of Arid Environments ◽

10.1016/j.jaridenv.2005.06.028 ◽

2006 ◽

Vol 65 (2) ◽

pp. 296-318 ◽

Cited By ~ 49

Author(s):

B.T. Bestelmeyer ◽

D.A. Trujillo ◽

A.J. Tugel ◽

K.M. Havstad

Keyword(s):

Vegetation Dynamics ◽

Arid Lands ◽

Multi Scale ◽

The Right ◽

Scale Classification

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Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/466 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ritesh Sarkhel ◽

Arnab Nandi

Keyword(s):

Turnaround Time ◽

Convolutional Network ◽

Multi Scale ◽

Deterministic Routing ◽

Pyramid Model ◽

Benchmark Datasets ◽

Work First ◽

Spatial Pyramid ◽

Scale Classification

Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround.

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