scholarly journals FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 243-249
Author(s):  
P. Ebel ◽  
S. Saha ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Optical Observations ◽  
Detection Methods ◽  
Data Set ◽  
Modal Data ◽  
Multi Temporal ◽  
Modal Change

Abstract. With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.

scholarly journals A Content-Based Remote Sensing Image Change Information Retrieval Model

2017 ◽  
Author(s):  
Caihong Ma ◽  
Wei Xia ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Qin Dai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Retrieval ◽  
Change Detection ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Prototype System ◽  
Retrieval Model ◽  
Model Framework ◽  
New Model

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

scholarly journals A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020 ◽  
Vol 12 (15) ◽  
pp. 2460 ◽  
Author(s):  
Yanan You ◽  
Jingyi Cao ◽  
Wenli Zhou
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Research Direction ◽  
Detection Methods ◽  
Urban Change ◽  
Multi Objective ◽  
Multi Temporal ◽  
Diverse Data ◽  
Favorable Conditions ◽  
Relationship Of

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

scholarly journals CORAL REEF HABITAT CHANGING ASSESSMENT OF DERAWAN ISLANDS, EAST KALIMANTAN, USING REMOTE SENSING DATA

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
MARLINA NURLIDIASARI ◽  
SYARIF BUDIMAN
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Coral Reefs ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Landsat Tm ◽  
East Kalimantan ◽  
Marine Diversity ◽  
Multi Temporal ◽  
Remote Sensing Techniques

Coral reefs in Dcrawan Islands are astonishingly rich in the marine diversity. However, these reefs are threatened by humans. Destructive fishing methods, such as trawl, blasting and cyanide fishing practise, are found to be the main cause of this degradation. The coral reefs habitat reduction is also caused by tourism activities due to trampling over the reef and charging organic and anorganic wastes. The capabilities of satellite remote sensing techniques combined with field data collection have been assessed for the coral reef mapping and the change detection of Derawan Island. Multi-temporal Landsat TM and ETM images (1991 and 2002) have been used. Comparison of the classified images of 1991 and 2002 shows spatial changes of the habitat. The changes were in accordance with the known changes in the reef conditions. The analysis shows the decrease of the coral reef and patchy seagrass percentage, while the increase of the algae composite and patchy reef percentage. Keywords : Coral Reef, Change Detection, Landsat-TM, Derawan

Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Jiaqi Zhao ◽  
Yong Zhou ◽  
Boyu Shi ◽  
Jingsong Yang ◽  
Di Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Feature Maps ◽  
Attention Network ◽  
Modal Data ◽  
Sensing Data ◽  
Multi Stage

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS) 2 -Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method.

scholarly journals A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data

2014 ◽  
Vol XL-7 ◽  
pp. 15-18 ◽  
Author(s):  
U. H. Atasever ◽  
P. Civicioglu ◽  
E. Besdok ◽  
C. Ozkan
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Backtracking Search ◽  
Search Optimization ◽  
Detection Approach ◽  
Multi Temporal ◽  
Backtracking Search Optimization Algorithm

Change detection is one of the most important subjects of remote sensing discipline. In this paper, a new unsupervised change detection approach is proposed for multi-temporal remotely sensed optic imagery. This approach does not require any prior information about changed and unchanged pixels. The approach is based on Discrete Wavelet Transform (DWT) based image fusion and Backtracking Search Optimization Algorithm (BSA). In the first step of the approach, absolute-valued difference image and absolute-valued log-ratio image is calculated from co-registered and radiometrically corrected multi-temporal images. Then, these difference images are fused using DWT. The fused image is filtered by median filter for edge information preservation and by wiener filter for image smoothing. Then, a min-max normalization is applied to the filtered data. The normalized data is clustered into two groups with BSA as changed and unchanged pixels by minimizing an objective function, unlike classical methods using CVA, PCA, FCM or K-means techniques. To show effectiveness of proposed approach, two remote sensing data sets, Sardinia and Mexico, are used. False Alarm, Missed Alarm, Total Alarm and Total Error Rate are selected as performance criteria to evaluate the effectiveness of new approach using ground truth images. Experimental results show that proposed approach is effective for unsupervised change detection of optical remote sensing data.

scholarly journals SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network

2021 ◽  
Vol 13 (18) ◽  
pp. 3750
Author(s):  
Ruizhe Shao ◽  
Chun Du ◽  
Hao Chen ◽  
Jun Li
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Auxiliary Information ◽  
Image Data ◽  
Detection Task ◽  
Detection Methods ◽  
Change Detection Task ◽  
Remote Sensing Images ◽  
Data Set ◽  
Dual Channel

Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.

Sign in / Sign up

Export Citation Format

Share Document