Detection of Alteration Induced by Onshore Gas Seeps from ASTER and WorldView-2 Data

Salati, Sanaz; Ruitenbeek, F.J.A. van; Meer, Freek D. van der; Naimi, Babak

doi:10.3390/rs6043188

Cited by 21 publications

(17 citation statements)

References 34 publications

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“…It has also proved that this method is spectrally stronger than other sharpening techniques for the fusion of WV2 multispectral bands with the panchromatic band [24]. The GSPS method has been widely applied in land cover classification [25], forest tree species classification [26], urban tree species classification [12], and detection of mineral alteration in a marly limestone formation [27]. In our study, the pan-sharpened image provides clear boundaries of tree crowns, and thus was used for further processing including segmentation and classification.…”

Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

et al. 2015

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Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes-classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images-were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%-82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%-20.2% for the CNU area and 4.7%-12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples.

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Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

et al. 2015

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“…(ii) Gas detection; i.e. detection of alteration caused by gas leakage using ASTER and WorldView-2 data [63].…”

Section: Worldview Satellitesmentioning

confidence: 99%

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Xiao

Chen

et al. 2018

Preprint

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Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

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“…Therefore, HSI is a tool that is well suited for a variety of remote sensing applications, such as spectral unmixing [7], classification [8], and target detection [9,10]. Anomaly detection is an important branch of HSI processing and subpixel domain; it can detect targets with spectral differences from the surrounding environment without prior spectral information, it has important applications in military defense [11,12], agriculture [13,14], mineral reconnaissance [15,16], public safety [17], and environmental monitoring [18][19][20], and has become a research hotspot in the field of hyperspectral target detection.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Spatial Density Background Purification

Liao

et al. 2019

Remote Sensing

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In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

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Detection of Alteration Induced by Onshore Gas Seeps from ASTER and WorldView-2 Data

Cited by 21 publications

References 34 publications

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Hyperspectral Anomaly Detection via Spatial Density Background Purification

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