A change detection model based on neighborhood correlation image analysis and decision tree classification

By Jungho Im, J. R. Jensen. 2005

358 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/369b8b29182ba1a13fbdf35ac6113d6ee1a29dd0

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

In Ecology

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

In Media Technology

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

Citations in the corpus

• Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “Off-nadir view angles, which result in geometric distortion of trees and high contrast between sunlit and shadow portions of tree crowns, also complicate the use of change detection methods when applied to such data (Peddle et al. 2003; Im and Jensen 2005).”
• A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “Further work showed that this same 242 comparison method may also be applied to multi-temporal image-objects (Im et al., 2008); although no 243 significant improvement was found when compared to the kernel based approach. When working with VHR 244 imagery several researchers have incorporated measur”
• Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “Different classification algorithms, such as decision-tree and a nearest neighbor classifier (Im and Jensen, 2005), fuzzy classification (Durieux et al., 2008), and maximum likelihood classification (MLC), are used in OBIA.”
• 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.”
• CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, methodology): “Other machine learning techniques applied for change detection are: decision tree (Im and Jensen, 2005), genetic programming (Makkeasorn et al., 2009), random forest (Smith, 2010), cellular automata (Yang et al., 2008) and deep neural networks (Chu et al., 2016).”
• 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].”
• Remote sensing imagery in vegetation mapping: a review (2008, result): “The similar result was also noted by Im and Jensen (2005) who used a three-channel neighborhood correlation image model to detect vegetation changes through the relation of pixels and their contextual neighbors.”