Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.

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SAR Image Change Detection Based on Correlation Kernel and Multistage Extreme Learning Machine

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2578438 ◽

2016 ◽

Vol 54 (10) ◽

pp. 5993-6006 ◽

Cited By ~ 15

Author(s):

Lu Jia ◽

Ming Li ◽

Peng Zhang ◽

Yan Wu

Keyword(s):

Change Detection ◽

Extreme Learning Machine ◽

Sar Image ◽

Correlation Kernel ◽

Image Change Detection ◽

Learning Machine

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TCDNet: Trilateral Change Detection Network for Google Earth Image

Remote Sensing ◽

10.3390/rs12172669 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2669

Author(s):

Junhao Qian ◽

Min Xia ◽

Yonghong Zhang ◽

Jia Liu ◽

Yiqing Xu

Keyword(s):

Deep Learning ◽

Change Detection ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

Google Earth ◽

Detection Methods ◽

Global Information ◽

Supervised Methods ◽

Image Pairs ◽

Main Module

Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.

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Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0011 ◽

2017 ◽

Vol 26 (1) ◽

pp. 185-195 ◽

Cited By ~ 3

Author(s):

Jie Wang ◽

Liangjian Cai ◽

Xin Zhao

Keyword(s):

Extreme Learning Machine ◽

State Of The Art ◽

Multiple Instance Learning ◽

Training Data ◽

Data Sets ◽

Rbf Kernel ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Hidden Layer ◽

Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

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Change Detection in Aerial Images Using Three-Dimensional Feature Maps

Remote Sensing ◽

10.3390/rs12091404 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1404

Author(s):

Saleh Javadi ◽

Mattias Dahl ◽

Mats I. Pettersson

Keyword(s):

Change Detection ◽

Sampling Rate ◽

Three Dimensional ◽

Image Resolution ◽

Aerial Images ◽

Aerial Image ◽

Detection Methods ◽

Surveillance Systems ◽

Feature Maps ◽

Aerial Imaging

Interest in aerial image analysis has increased owing to recent developments in and availability of aerial imaging technologies, like unmanned aerial vehicles (UAVs), as well as a growing need for autonomous surveillance systems. Variant illumination, intensity noise, and different viewpoints are among the main challenges to overcome in order to determine changes in aerial images. In this paper, we present a robust method for change detection in aerial images. To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time. In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’s height information, are investigated through a mathematical model. To exhibit its applicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising. The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.

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Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine

Journal of Applied Remote Sensing ◽

10.1117/1.3518096 ◽

2010 ◽

Vol 4 (1) ◽

pp. 043551 ◽

Cited By ~ 18

Author(s):

Min Han

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Extreme Learning Machine ◽

Urban Region ◽

Learning Machine ◽

Spot 5

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An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Journal of Hydrology ◽

10.1016/j.jhydrol.2018.11.069 ◽

2019 ◽

Vol 569 ◽

pp. 387-408 ◽

Cited By ~ 201

Author(s):

Zaher Mundher Yaseen ◽

Sadeq Oleiwi Sulaiman ◽

Ravinesh C. Deo ◽

Kwok-Wing Chau

Keyword(s):

Extreme Learning Machine ◽

River Flow ◽

State Of The Art ◽

Research Direction ◽

Future Research ◽

Future Research Direction ◽

Machine Model ◽

Practical Applications ◽

River Flow Forecasting ◽

Learning Machine

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Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

Sustainability ◽

10.3390/su13148009 ◽

2021 ◽

Vol 13 (14) ◽

pp. 8009

Author(s):

Isa Ebtehaj ◽

Keyvan Soltani ◽

Afshin Amiri ◽

Marzban Faramarzi ◽

Chandra A. Madramootoo ◽

...

Keyword(s):

Extreme Learning Machine ◽

Land Surface ◽

Weather Forecasting ◽

Google Earth ◽

Shortwave Radiation ◽

Energy Budgets ◽

Percentage Error ◽

Estimation Uncertainty ◽

Learning Machine ◽

Hidden Neurons

Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.

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