Multiple support vector machines for land cover change detection: An application for mapping urban extensions

By Hassiba Nemmour, Youcef Chibani. 2006

336 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/e9d95c2eab5073e94cac9c1ff678d04e09b93c0d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Liangpei Zhang
2. Zhenwei Shi
3. Qian Du
4. Xiaoping Liu
5. Fei Wang
6. Mengxi Liu
7. Shengchen Li
8. Qian Shi

In Ecology

1. Liangpei Zhang
2. Zhenwei Shi
3. Jingfeng Huang
4. Qian Du
5. Xiaoping Liu
6. Fei Wang
7. Mengxi Liu
8. Shengchen Li

In Media Technology

1. Liangpei Zhang
2. Zhenwei Shi
3. Qian Du
4. Xuelong Li
5. Jingfeng Huang
6. Qi Wang
7. George Alan Blackburn
8. Xiaoping Liu

Citations in the corpus

• Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves (2017, methodology): “…already been used in land cover 108 classification (Gao et al., 2015; Hong et al., 2015; Zhang et al., 2015), quantifying 109 vegetation stress (Adjorlolo et al., 2015; Behmann et al., 2014) and land cover change 110 detection (Hichri et al., 2013; Hussain et al., 2013; Nemmour & Chibani, 2006).”
• Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, methodology): “…machine learning approaches were adopted in the late 1990s, including neural networks (Dai & Khorram, 1999; Liu & Lathrop, 2002), boosted/bagged decision trees (Rogan et al., 2003; Schneider et al., 2003, 2005), and support vector machines (Griffiths et al., 2010; Nemmour & Chibani, 2006).”
• Support vector machines in remote sensing: A review (2011, methodology): “Nemmour and Chibani (2006) studied urban change detection using Landsat scenes and found SVM methods outperformed backpropagation neural networks.”
• GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection (2019, methodology): “This is also the case of classification of differential features [21] and compound classification [1].”
• Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, neutral): “…during the learning phase increases polynomially with the increase of data dimensionality (Habib et al., 2009) Land cover change detection (Nemmour and Chibani, 2006) Forest cover change analysis (Huang et al., 2008) Decision Tree Non-Parametric and no assumption on data distribution Can…”
• A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (2020, methodology): “Classifiers such as support vector machines (SVMs) [16,17] and decision trees (DTs) [13], and graphical models, such as Markov random field models [18,19] and conditional random field models [20], have also been applied in CD.”
• A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, methodology): “Accordingly, machine-learning algorithms, which can learn from a part of labeled samples to get an automated decision model, have been widely used for remote sensing CD [10], including support vector machine [11], decision tree [12], and random forest [13].”