Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral image classification uses the huge amount of information provided by spectral images to identify objects in the scene of interest. In this sense, spectral images typically contain redundant information that is removed in later processing stages.
Nelson Diaz +3 more
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Hyperspectral image classification via contextual deep learning [PDF]
EURASIP Journal on Image and Video Processing, 2015 AbstractBecause the reliability of feature for every pixel determines the accuracy of classification, it is important to design a specialized feature mining algorithm for hyperspectral image classification. We propose a feature learning algorithm, contextual deep learning, which is extremely effective for hyperspectral image classification.
Ma, Xiaorui, Geng, Jie, Wang, Hongyu
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Image Classification with Rejection using Contextual Information [PDF]
, 2015 We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information. Rejection is desired in image-classification applications that require a robust classifier but not the classification of the entire image.
Condessa, Filipe +4 more
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CONTEXTUAL IMAGE CLASSIFICATION APPROACH FOR MONITORING OF AGRICULTURAL LAND COVER BY SUPPORT VECTOR MACHINES AND MARKOV RANDOM FIELDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 The main idea of this paper is to integrate the non-contextual support vector machines (SVM) classifiers with Markov random fields (MRF) approach to develop a contextual framework for monitoring of agricultural land cover.
H. Vahidi, E. Monabbati
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Shallow Parallel CNNs for contextual remote sensing image classification [PDF]
, 2022 Abstract In this paper we present a new neural network structure that can better learn to classify remote sensing images of moderate and high spatial resolution where the main source of information about desired objects are the pixels themselves and the tight neighborhood.
Bassam Abdellatif +2 more
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Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels [PDF]
Remote Sensing, 2014 In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse ...
Jilan Feng, Zongjie Cao, Yiming Pi
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From ImageNet to Image Classification: Contextualizing Progress on Benchmarks [PDF]
, 2020 Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset.
Tsipras, Dimitris +4 more
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A Bilevel Contextual MRF Model for Supervised Classification of High Spatial Resolution Remote Sensing Images [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019 Markov random field (MRF) based methods have been widely used in high spatial resolution (HSR) image classification. However, many existing MRF-based methods put more emphasis on pixel level contexts while less on superpixel level contextual information.
Yu Shen +3 more
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Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification [PDF]
, 2022 Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy under low computational complexity,
Patacchiola, Massimiliano +5 more
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Contextual Prediction Difference Analysis for Explaining Individual Image Classifications [PDF]
, 2019 Much effort has been devoted to understanding the decisions of deep neural networks in recent years. A number of model-aware saliency methods were proposed to explain individual classification decisions by creating saliency maps. However, they are not applicable when the parameters and the gradients of the underlying models are unavailable.
Gu, Jindong, Tresp, Volker
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