CMLBPIncoherent: a New Contextual Image Descriptor for Scene Classification
Anais do 14º Simpósio Brasileiro de Automação Inteligente, 2019 Matheus Vieira Lessa Ribeiro +1 more
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Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions [PDF]
, 2019 Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment.
Alvarez-Gila, Aitor +5 more
core +1 more source
RULE-BASED CLASSIFICATION OF A HYPERSPECTRAL IMAGE USING MSSC HIERARCHICAL SEGMENTATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for hyperspectral image analysis.
D. Akbari, A. R. Safari
doaj +1 more source
International Journal of Applied Earth Observations and Geoinformation, 2020 Classification of very high resolution imagery (VHRI) is challenging due to the difficulty in mining complex spatial and spectral patterns from rich image details.
Chenxiao Zhang +5 more
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Semisupervised Complex Network With Spatial Statistics Fusion for PolSAR Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Deep learning has achieved satisfactory results in polarimetric synthetic aperture radar (PolSAR) image classification, which requires a large number of labeled samples for training. However, in practice, labeling work is time-consuming and laborious. As
Yinyin Jiang +3 more
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Remote Sensing, 2020 In land cover mapping at a high spatial resolution, pixel values alone are not always sufficient to recognize the more complex classes. Contextual features (computed with a sliding kernel or other kind of spatial support) can be discriminating for ...
Dawa Derksen +2 more
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Right for the Right Reason: Training Agnostic Networks [PDF]
, 2018 We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network.
A Halevy +7 more
core +3 more sources
PolSAR Classification Using Contextual Based Locality Preserving Projection and Guided Filtering
International Journal of Information and Communication Technology Research, 2021 Contextual feature extraction is studied for polarimetric synthetic aperture radar (PolSAR) image classification in this work. The contextual locality preserving projection (CLPP) method is proposed for generation of contextual feature cubes using ...
Maryam Imani
doaj
AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered.
L. Albert, F. Rottensteiner, C. Heipke
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Higher Order Support Vector Random Fields for Hyperspectral Image Classification
ISPRS International Journal of Geo-Information, 2018 This paper addresses the problem of contextual hyperspectral image (HSI) classification. A novel conditional random fields (CRFs) model, known as higher order support vector random fields (HSVRFs), is proposed for HSI classification.
Junli Yang +3 more
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