Change detection with cross enhancement of high‐ and low‐level change‐related features

A Siamese convolutional neural network with high–low level feature fusion for change detection in remotely sensed images

Remote Sensing Letters ◽

10.1080/2150704x.2021.1892851 ◽

2021 ◽

Vol 12 (4) ◽

pp. 387-396

Author(s):

Hao Zhou ◽

Mi Zhang ◽

Xiangyun Hu ◽

Kun Li ◽

Jing Sun

Keyword(s):

Neural Network ◽

Change Detection ◽

Convolutional Neural Network ◽

Feature Fusion ◽

Remotely Sensed ◽

Low Level ◽

Remotely Sensed Images

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Object-level change detection based on full-scale image segmentation and its application to Wenchuan Earthquake

Science in China Series E ◽

10.1007/s11431-008-6017-y ◽

2008 ◽

Vol 51 (S2) ◽

pp. 110-122 ◽

Cited By ~ 13

Author(s):

JianYa Gong ◽

HaiGang Sui ◽

KaiMin Sun ◽

GuoRui Ma ◽

JunYi Liu

Keyword(s):

Image Segmentation ◽

Change Detection ◽

Wenchuan Earthquake ◽

Full Scale ◽

Level Change ◽

Object Level

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Leveraging Object Proposals for Object-Level Change Detection

2018 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2018.8500475 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sugimoto Takuma ◽

Tanaka Kanji ◽

Yamaguchi Kousuke

Keyword(s):

Change Detection ◽

Level Change ◽

Object Proposals ◽

Object Level

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Building-Level Change Detection from Large-Scale Historical Vector Data by Using Direct and a Three-Tier Post-classification Comparison

Computational Science and Its Applications – ICCSA 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-95168-3_20 ◽

2018 ◽

pp. 300-316

Author(s):

Masroor Hussain ◽

Dongmei Chen

Keyword(s):

Change Detection ◽

Large Scale ◽

Vector Data ◽

Level Change ◽

Building Level

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Object-level change detection in spectral imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.911113 ◽

2001 ◽

Vol 39 (3) ◽

pp. 553-561 ◽

Cited By ~ 48

Author(s):

G.G. Hazel

Keyword(s):

Change Detection ◽

Level Change ◽

Object Level

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Scalable Change Detection Using Place-Specific Compressive Change Classifiers

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0705 ◽

2019 ◽

Vol 23 (4) ◽

pp. 705-714

Author(s):

Kanji Tanaka ◽

Keyword(s):

Change Detection ◽

Large Scale ◽

Detection System ◽

Visual Image ◽

Level Change ◽

Specific Object ◽

Training Examples ◽

Classifier Learning ◽

Object Level

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. In this paper, we present an efficient approach of change-classifier-learning, more specifically, in the proposed approach, a collection of place-specific change classifiers is employed. Our approach requires the memorization of only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Furthermore, through the proposed approach the most recent map can be incorporated into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. The proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.

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Low-level visual saliency does not predict change detection in natural scenes

Journal of Vision ◽

10.1167/7.10.3 ◽

2007 ◽

Vol 7 (10) ◽

pp. 3 ◽

Cited By ~ 63

Author(s):

Jonathan A. Stirk ◽

Geoffrey Underwood

Keyword(s):

Change Detection ◽

Visual Saliency ◽

Natural Scenes ◽

Low Level

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Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.3029460 ◽

2020 ◽

Vol 13 ◽

pp. 6260-6276

Author(s):

Xin Wang ◽

Peijun Du ◽

Dongmei Chen ◽

Sicong Liu ◽

Wei Zhang ◽

...

Keyword(s):

Change Detection ◽

Low Level ◽

High Level

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What You Fear Will Appear

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000058 ◽

2010 ◽

Vol 57 (6) ◽

pp. 470-475 ◽

Cited By ~ 11

Author(s):

Nathalie Peira ◽

Armita Golkar ◽

Maria Larsson ◽

Stefan Wiens

Keyword(s):

Change Detection ◽

Ecological Validity ◽

Real Life ◽

Visual Input ◽

Low Level ◽

Blank Interval ◽

Object Features

Various experimental tasks suggest that fear guides attention. However, because these tasks often lack ecological validity, it is unclear to what extent results from these tasks can be generalized to real-life situations. In change detection tasks, a brief interruption of the visual input (i.e., a blank interval or a scene cut) often results in undetected changes in the scene. This setup resembles real-life viewing behavior and is used here to increase ecological validity of the attentional task without compromising control over the stimuli presented. Spider-fearful and nonfearful women detected schematic spiders and flowers that were added to one of two identical background pictures that alternated with a brief blank in between them (i.e., flicker paradigm). Results showed that spider-fearful women detected spiders (but not flowers) faster than did nonfearful women. Because spiders and flowers had similar low-level features, these findings suggest that fear guides attention on the basis of object features rather than simple low-level features.

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