A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. <br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

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Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

Remote Sensing ◽

10.3390/rs10071134 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1134 ◽

Cited By ~ 5

Author(s):

Hossein Vahidi ◽

Brian Klinkenberg ◽

Brian Johnson ◽

L. Moskal ◽

Wanglin Yan

Keyword(s):

High Resolution ◽

Template Matching ◽

False Negative ◽

Volunteered Geographic Information ◽

Remotely Sensed ◽

Geographic Information ◽

Remotely Sensed Data ◽

Individual Tree ◽

Training Samples ◽

Very High

This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.

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Detection of Urban Features Using Morphological Based Segmentation and Very High Resolution Remotely Sensed Data

Machine Vision and Advanced Image Processing in Remote Sensing ◽

10.1007/978-3-642-60105-7_25 ◽

1999 ◽

pp. 271-284 ◽

Cited By ~ 10

Author(s):

Martino Pesaresi ◽

Ioannis Kanellopoulos

Keyword(s):

High Resolution ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Very High

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Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs12061012 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1012 ◽

Cited By ~ 2

Author(s):

Cheng Shi ◽

Zhiyong Lv ◽

Xiuhong Yang ◽

Pengfei Xu ◽

Irfana Bibi

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Image Classification ◽

Supervised Learning ◽

Remote Sensing Image ◽

Classification Performance ◽

Remote Sensing Images ◽

Training Set ◽

Remote Sensing Image Classification ◽

Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

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Robust automatic recognition system of manmade areas using morphological segmentation and very-high-resolution remotely sensed data

10.1117/12.331870 ◽

1998 ◽

Author(s):

M. Pesaresi ◽

Ioannis Kanellopoulos

Keyword(s):

High Resolution ◽

Recognition System ◽

Remotely Sensed ◽

Automatic Recognition ◽

Remotely Sensed Data ◽

Morphological Segmentation ◽

Very High

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A Comparative Study of Land Cover Classification Techniques for “Farmscapes” Using Very High Resolution Remotely Sensed Data

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.80.5.461 ◽

2014 ◽

Vol 80 (5) ◽

pp. 461-470 ◽

Cited By ~ 4

Author(s):

Niva Kiran Verma ◽

David W. Lamb ◽

Nick Reid ◽

Brian Wilson

Keyword(s):

High Resolution ◽

Land Cover ◽

Comparative Study ◽

Land Cover Classification ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Classification Techniques ◽

Very High

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Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Remote Sensing ◽

10.3390/rs11030284 ◽

2019 ◽

Vol 11 (3) ◽

pp. 284 ◽

Cited By ~ 1

Author(s):

Linglin Zeng ◽

Shun Hu ◽

Daxiang Xiang ◽

Xiang Zhang ◽

Deren Li ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Regional Scale ◽

Remotely Sensed ◽

Temporal Variations ◽

Training Data ◽

Estimation Accuracy ◽

Learning Approaches ◽

Remotely Sensed Data ◽

Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

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Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images

Forests ◽

10.3390/f12010066 ◽

2021 ◽

Vol 12 (1) ◽

pp. 66

Author(s):

Kirill A. Korznikov ◽

Dmitry E. Kislov ◽

Jan Altman ◽

Jiří Doležal ◽

Anna S. Vozmishcheva ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Satellite Images ◽

Russian Far East ◽

Pinus Koraiensis ◽

Accuracy Score ◽

Learning Approaches ◽

Individual Tree ◽

Populus Suaveolens ◽

Very High

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.

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