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

Abstract

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...

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

“…The reason for this interest lies in the fact that anomaly detection based on Kittler's Taxonomy is an innovative and promising pattern recognition strategy, which can be used to help machines recognize the occurrence of problems, e.g., environmental disasters, in different contexts present in remote sensing images. The ADS-KT was applied for the recognition of problems monitored by remote sensing for the first time in [17]. The study presents an approach to decision-making systems, based on which machines would be closer to recognizing contexts in which the problems are embedded.…”

“…For example, and in relation to the research question, we hypothesized that if transformations in the original data space (e.g., reducing the attribute values) impact the ADS-KT, even if dimensions are maintained, dimensionality reduction also impacts the same strategy, as the original data space is transformed and reduced as a consequence of dimensionality reduction. Therefore, this study aims to analyze the behavior of the application of the ADS-KT, presented by Dias et al [17], in a low-dimensional space, obtained from a dimensionality reduction technique, or in a transformed data space. This objective is important to help us understand if and how dimensionality reduction impacts Kittler's Taxonomy-based anomaly detection, when it is applied to an image from remote sensing and especially when an environmental disaster is used as a case study.…”

Geo-Informatics in Resource Management

“…Geo-informatic research involves using new information methods and technologies, software suites, geo-databases, the internet and developing software. These techniques are successfully used in a broad range of areas of knowledge like urban planning [1,2], conservation and promotion of cultural heritage [3,4], marketing [5], agriculture [6,7], forestry [8,9], air quality monitoring [10,11] and water pollution [12,13] among many others.…”

Editorial for the Special Issue “Remote Sensing of Large Rivers”

“…(2) methods concerning water quality, such as Total suspended solids (TSS) and pollutants [15][16][17]; and (3) contributions in mapping bathymetry [18]. We published two application papers that used existing remote sensing techniques as a tool to better characterize large river systems, both in the Asian continent [19,20].…”

“…Another study in our special issue [17] developed a semi-automated model to detect water pollution from static optical remote sensing images by systematically detecting outlier signatures from the image. They demonstrated the model performance using case studies throughout different settings of large rivers, through successfully detecting water-related hazards such as pollution/landslides, or oil spills.…”