Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

Chen, Chi; Wang, Jinhu; Song, Shuang; Peng, Xiangyang; Huang, Ronggang

doi:10.3390/rs10040613

Cited by 69 publications

(51 citation statements)

References 51 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 shows how the cropping was done for each Landsat 8 scene. In order to quantitatively validate the proposed strategy, we used the metrics accuracy, precision, recall [15], and F-measure [82] for which, TP are true positives, i.e., images in which incongruences are truly detected; TN are true negatives, i.e., images in which congruences are truly detected; FN are false negatives, i.e., images in which congruences are falsely detected; FP are false positives, i.e., images in which incongruences are falsely detected. Accuracy measures the efficiency of results and is represented by Equation (23), for which M is the number of images.…”

Section: Resultsmentioning

confidence: 99%

Section: Study Accuracy Precision Recall F-measurementioning

confidence: 98%

“…Ours~99.78%~73.96% 100.00% 85.04% [98] 99.59% ------------ [81] 99.20% 91.85% 53.55% 67.66% [99] 99.14% ------------ [76] 98.49% 83.84% 83.66% 83.76% [80] 98.00% ------------ [ Our strategy achieved the highest value of F-measure among the presented studies, except for [25] and [82]. However, [25] and [82] are very specific case studies. Whereas in this paper and in [66,[76][77][78][80][81][82][98][99][100] the areas of study are heterogeneous, in [25], results were presented only related to an ice shelf in the Antarctic region, which is a homogeneous area of study.…”

Section: Study Accuracy Precision Recall F-measurementioning

confidence: 98%

“…Most current research focuses on outlier detection, such as [1,14,[19][20][21][22][23][24][25]44,[51][52][53][54]66,[76][77][78][80][81][82], however we discuss below only those research which are more relevant to this article. An outlier-based change detection method to detect abnormal points in multi-scale time-series images from remote sensing was proposed by Yin Shoujing et al in [76].…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

“…An automatic clearance anomaly detection method was proposed by Chen et al in [82]. The clearance anomaly detection measures the distance between power lines and other objects (e.g., trees and buildings) to evaluate whether the clearance is within the safe range.…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

See 4 more Smart Citations

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

View full text Add to dashboard Cite

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

show abstract

Section: Resultsmentioning

confidence: 99%