MFCNET: End-to-End Approach for Change Detection in Images

Chen, Ying; Ouyang, Xu; Agam, Gady

doi:10.1109/icip.2018.8451392

Cited by 17 publications

(18 citation statements)

References 16 publications

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“…However, this approach is lacking the appropriate modeling of data's temporal pattern. Several other methods that use CNN in Siamese settings are [33,10,9,7,16]. Papadomanolaki et al [23] proposed a convolutional LSTM based approach that takes in five dates to detect changes in first and last date's images.…”

Section: Related Workmentioning

confidence: 99%

QFabric: Multi-Task Change Detection Dataset

Verma

Panigrahi²,

Gupta³

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period.This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://sagarverma.github.io/qfabric Land Cleared Construction Midway Construction Midway Construction Done OperationalCommercial Industrial,

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Section: Related Workmentioning

confidence: 99%

QFabric: Multi-Task Change Detection Dataset

Verma

Panigrahi²,

Gupta³

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…Most algorithms circumvented the problem of the scarcity of training data through transfer learning by using pretrained networks to generate pixel descriptors (El Amin et al, 2016Sakurada & Okatani, 2015). Fully convolutional networks trained end-to-end to perform change detection have recently been proposed by several authors independently, usually using Siamese architectures (Chen et al, 2018;Daudt et al, 2018aDaudt et al, , 2019cGuo et al, 2018;Zhan et al, 2017). Unsupervised (Alvarez et al, 2020;Luppino et al, 2020;Saha et al, 2019 and semi-supervised alternatives have also been proposed to cope with the scarcity of accurately labelled data.…”

Section: Related Workmentioning

confidence: 99%

“…Many variations of convolutional neural networks (CNNs) (LeCun et al, 1998), notably fully convolutional networks (FCNs) (Long et al, 2015), have recently achieved excellent performances in change detection tasks (Chen et al, 2018;Daudt et al, 2018aDaudt et al, , 2019cGuo et al, 2018). These methods require large amounts of training data to perform supervised training of the proposed networks (LeCun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised change detection using guided anisotropic diffusion

et al. 2021

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Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these datasets are easier to acquire, the data are frequently noisy and unreliable, which is motivating research on weakly supervised learning techniques. In this paper we propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results using the input images as guides to perform edge preserving filtering. We then show its potential in two weakly-supervised learning strategies tailored for change detection. The first strategy is an iterative learning method that combines model optimisation and data cleansing using GAD to extract the useful information from a large scale change detection dataset generated from open vector data. The second one incorporates GAD within a novel spatial attention layer that increases the accuracy of weakly supervised networks trained to perform pixel-level predictions from image-level labels. Improvements with respect to state-of-the-art are demonstrated on 4 different public datasets.

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“…These works either focus on the image differencing method [5,15,16], or put effort on decision function [7,8]. More recent works [35,10] take use of convolutional neural networks to perform binary change detection. This task is only able to determine whether a region has changes, without telling the type of change.…”

Section: Binary Change Detectionmentioning

confidence: 99%

Semantic Change Pattern Analysis

Cheng,

Zhang,

Lei

et al. 2020

Preprint

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Change detection is an important problem in vision field, especially for aerial images. However, most works focus on traditional change detection, i.e., where changes happen, without considering the change type information, i.e., what changes happen. Although a few works have tried to apply semantic information to traditional change detection, they either only give the label of emerging objects without taking the change type into consideration, or set some kinds of change subjectively without specifying semantic information. To make use of semantic information and analyze change types comprehensively, we propose a new task called semantic change pattern analysis for aerial images. Given a pair of co-registered aerial images, the task requires a result including both where and what changes happen. We then describe the metric adopted for the task, which is clean and interpretable. We further provide the first well-annotated aerial image dataset for this task.Extensive baseline experiments are conducted as reference for following works. The aim of this work is to explore high-level information based on change detection and facilitate the development of this field with the publicly available dataset.

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MFCNET: End-to-End Approach for Change Detection in Images

Cited by 17 publications

References 16 publications

QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

Weakly supervised change detection using guided anisotropic diffusion

Semantic Change Pattern Analysis

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