Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

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An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

Remote Sensing ◽

10.3390/rs13010065 ◽

2020 ◽

Vol 13 (1) ◽

pp. 65

Author(s):

Jingtao Li ◽

Yonglin Shen ◽

Chao Yang

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Short Term Memory ◽

Complete Classification ◽

Support Vector ◽

Classification Models ◽

Training Samples ◽

Agricultural Applications ◽

Crop Classification ◽

Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

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The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4747-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4747-4765 ◽

Cited By ~ 8

Author(s):

Clara Linés ◽

Micha Werner ◽

Wim Bastiaanssen

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Data Sets ◽

Drought Management ◽

Ebro Basin ◽

Sensing Data ◽

Drought Impacts ◽

Management Plans ◽

Drought Indicators ◽

Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

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Optimized selection of training samples for One-Class Neural Network classifier

2014 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2014.6889429 ◽

2014 ◽

Cited By ~ 4

Author(s):

Bilal Hadjadji ◽

Youcef Chibani

Keyword(s):

Neural Network ◽

Neural Network Classifier ◽

Training Samples ◽

Selection Of

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New active learning algorithms for near-infrared spectroscopy in agricultural applications

at - Automatisierungstechnik ◽

10.1515/auto-2020-0143 ◽

2021 ◽

Vol 69 (4) ◽

pp. 297-306

Author(s):

Julius Krause ◽

Maurice Günder ◽

Daniel Schulz ◽

Robin Gruna

Keyword(s):

Active Learning ◽

Near Infrared ◽

Agricultural Products ◽

Training Data ◽

Calibration Model ◽

Learning Approaches ◽

Training Samples ◽

Agricultural Applications ◽

Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

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Improved Training for Machine Learning: The Additional Potential of Innovative Algorithmic Approaches.

10.5194/egusphere-egu21-4683 ◽

2021 ◽

Author(s):

Octavian Dumitru ◽

Gottfried Schwarz ◽

Mihai Datcu ◽

Dongyang Ao ◽

Zhongling Huang ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Test Data ◽

Satellite Images ◽

Training Data ◽

Data Selection ◽

Generative Adversarial Networks ◽

Radar Images ◽

Basic Work ◽

Selection Of

During the last years, much progress has been reached with machine learning algorithms. Among the typical application fields of machine learning are many technical and commercial applications as well as Earth science analyses, where most often indirect and distorted detector data have to be converted to well-calibrated scientific data that are a prerequisite for a correct understanding of the desired physical quantities and their relationships.However, the provision of sufficient calibrated data is not enough for the testing, training, and routine processing of most machine learning applications. In principle, one also needs a clear strategy for the selection of necessary and useful training data and an easily understandable quality control of the finally desired parameters.At a first glance, one could guess that this problem could be solved by a careful selection of representative test data covering many typical cases as well as some counterexamples. Then these test data can be used for the training of the internal parameters of a machine learning application. At a second glance, however, many researchers found out that a simple stacking up of plain examples is not the best choice for many scientific applications.To get improved machine learning results, we concentrated on the analysis of satellite images depicting the Earth&#8217;s surface under various conditions such as the selected instrument type, spectral bands, and spatial resolution. In our case, such data are routinely provided by the freely accessible European Sentinel satellite products (e.g., Sentinel-1, and Sentinel-2). Our basic work then included investigations of how some additional processing steps &#8211; to be linked with the selected training data &#8211; can provide better machine learning results.To this end, we analysed and compared three different approaches to find out machine learning strategies for the joint selection and processing of training data for our Earth observation images:<ul><li>One can optimize the training data selection by adapting the data selection to the specific instrument, target, and application characteristics [1].</li> <li>As an alternative, one can dynamically generate new training parameters by Generative Adversarial Networks. This is comparable to the role of a sparring partner in boxing [2].</li> <li>One can also use a hybrid semi-supervised approach for Synthetic Aperture Radar images with limited labelled data. The method is split in: polarimetric scattering classification, topic modelling for scattering labels, unsupervised constraint learning, and supervised label prediction with constraints [3].</li> </ul>We applied these strategies in the ExtremeEarth sea-ice monitoring project (http://earthanalytics.eu/). As a result, we can demonstrate for which application cases these three strategies will provide a promising alternative to a simple conventional selection of available training data.[1] C.O. Dumitru et. al, &#8220;Understanding Satellite Images: A Data Mining Module for Sentinel Images&#8221;, Big Earth Data, 2020, 4(4), pp. 367-408.[2] D. Ao et. al., &#8220;Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X&#8221;, Remote Sensing, 2018, 10(10), pp. 1-23.[3] Z. Huang, et. al., "HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Images", IEEE Transactions on Geoscience and Remote Sensing, 2020, pp.1-18.

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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs10121934 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1934 ◽

Cited By ~ 11

Author(s):

Bao-Di Liu ◽

Wen-Yang Xie ◽

Jie Meng ◽

Ye Li ◽

Yanjiang Wang

Keyword(s):

Remote Sensing ◽

Test Sample ◽

Remote Sensing Image ◽

Image Features ◽

Superior Performance ◽

Collaborative Representation ◽

Great Success ◽

Specific Class ◽

Remote Sensing Images ◽

Training Samples

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

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