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

Autor(es)

Dias, Mauricio Araujo ^[1] ; da Silva, Erivaldo Antonio ^[2] ; de Azevedo, Samara Calcado ^[3] ; Casaca, Wallace ^[4] ; Statella, Thiago ^[5] ; Negri, Rogerio Galante ^[6]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ, UNESP, Sch Sci & Technol, Dept Math & Comp Sci, Campus Presidente Prudente, BR-19060900 Presidente Prudente, SP - Brazil

^[2] Sao Paulo State Univ, UNESP, Sch Sci & Technol, Dept Cartog, Campus Presidente Prudente, BR-19060900 Presidente Prudente, SP - Brazil

^[3] Univ Fed Itajuba, Nat Resources Dept, Av BPS 1303, BR-37500903 Itajuba, MG - Brazil

^[4] Sao Paulo State Univ, UNESP, Dept Energy Engn, Campus Rosana, BR-19274000 Rosana, SP - Brazil

^[5] Fed Inst Educ Sci & Technol Mato Grosso IFMT, 95 Zulmira Canavarro, BR-78002520 Cuiaba, MT - Brazil

^[6] Sao Paulo State Univ, UNESP, Sci & Technol Inst, Dept Environm Engn, Campus Sao Jose dos Campos, BR-12247004 Sao Jose Dos Campos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 12, n. 1 JAN 1 2020.
• Citações Web of Science: 3

Resumo

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. (AU)

• Processo FAPESP: 16/24185-8 - Detecção, análise e localização de anomalias: um estudo de caso sobre imagens digitais estáticas de sensoriamento remoto aplicado à Cartografia
• Beneficiário: Maurício Araújo Dias
• Modalidade de apoio: Bolsas no Exterior - Pesquisa