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Image processing and AI techniques for climate change detection using remote sensing: a comprehensive review. [PDF]

Front Artif Intell
Agarwal A, Kumar S, Rajini GK, Vairavasundaram I.   +3 more
europepmc   +1 more source

Trade-Offs of Conservation Fencing in Western Serengeti: Enhancing Agricultural Security While Navigating Unintended Consequences on Land-Use Dynamics. [PDF]

Ecol Evol
Kimaro MH, Sloots MI, Snyder KD, Mbise NL, Kakengi VA, Di Nicola W, Olff H.   +6 more
europepmc   +1 more source

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An expert system for land cover classification

IEEE Transactions on Geoscience and Remote Sensing, 1995
A framework to represent a broad class of problems in the analysis of remote sensing imagery is proposed, and an inference engine to tackle such problems is derived. A simple model for spectral knowledge representation is used along with a method for quantification of knowledge through an evidential approach. An automatic knowledge extraction technique
B. Kartikeyan, Kantilal L. Majumder, A. R. Dasgupta   +2 more
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Random Forests for land cover classification

Pattern Recognition Letters, 2006
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy considerably. The most widely used ensemble methods are boosting and bagging.
Pall Oskar Gislason, Jón Atli Benediktsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Dual Adversarial Networks for Land-Cover Classification

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020
River basin scene classification as an important application in the field of land-cover recognition has been arousing extensive concern. Traditional land-cover classification methods with multi-feature extractions on specific scene perform well on a single river basin, however, poorly address inter-basin classification owing to the varied texture shown
Jingyi An, Rongzhe Wei, Zhichao Zha, Bo Dong   +3 more
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The use of land cover change likelihood for improving land cover classification

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
The likelihood of transitions between pairs of land cover and land use classes in a given time interval and environmental context can be used to impose classification restrictions on an image or to evaluate results. This study presents a methodology for using the likelihood of transitions between classes to improve land cover classification, given a ...
Mariane Souza Reis, Sidnei J. S. Sant'Anna, Luciano Vieira Dutra, Maria Isabel Sobral Escada, Eliana Pantaleão   +4 more
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Land cover classification by SAR

Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium, 2002
A 7.5 km/spl times/12.4 km test site located in northern Michigan was used to evaluate the land-cover classification accuracy attainable by automatic classifiers using various combinations of SAR polarizations and frequency bands. The scene was imaged by the JPL AirSAR at L- and C-bands.
F. Ulaby, L.E. Pierce, M.C. Dobson, S. Chacon, K. Sarabandi   +4 more
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Accuracy and inaccuracy assessments in land-cover classification

IEEE Transactions on Geoscience and Remote Sensing, 1999
Several measures assessing accuracy of land-cover classification are available, e.g., overall and class-averaged accuracies. Also the kappa statistic is widely used for this purpose. The authors discuss properties of these criteria and point out that the kappa statistic has an unfavorable feature.
Ryuei Nishii, Shojiro Tanaka
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Random forests for land cover classification

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
In recent years, a number of works reported the use of combination of multiple classifiers to produce a single classification and demonstrated significant performance improvement. The resulting classifier, referred to as an ensemble classifier, is a set of classifiers whose individual decisions are combined by weighted or unweighted voting to classify ...
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Deep Aggregation Net for Land Cover Classification

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018
Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. In this paper, we propose a deep aggregation network for solving this task, which extracts and combines multi-layer features during the segmentation process.
Tzu-Sheng Kuo, Keng-Sen Tseng, Jia-Wei Yan, Yen-Cheng Liu, Yu-Chiang Frank Wang   +4 more
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