Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy

Abstract

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory

Characterization of Terrestrial Discharges into Coastal Waters with Thermal Imagery from a Hierarchical Monitoring Program

“…At sites where thermographic data alone are insufficient for the monitoring program, other approaches have been used. The fusion of optical data and synthetic aperture radar have been used for feature-based detection of environmental hazards [31,32], and ratios of multi-spectral bands have been used to detect surface contamination of soil and water [33,34]. Monitoring programs can also use remote sensing-based detection cyanobacteria together with knowledge of flow paths to make inferences of the impact and source of water pollution [35].…”

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

“…SAR datasets alone or optical datasets alone usually do not perform well in some applications [1,2]. However, the fusion of SAR and optical images can provide complementary information that is beneficial for the applications [3][4][5].…”

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

“…Likewise, low-resolution temporally-dense series can be fused with high-resolution, but temporally sparse ones to simulate a temporal-spatial full-resolution sequence [43]. The monitoring of forests [21], soil moisture [2], environmental hazards [12] and other processes can be also carried out effectively by fusing SAR and optical time series. Finally, works that mix all three aspects, resolution, time and sensor, can also be found in the literature [11,22,44].…”

“…According to the taxonomy given in [5] data fusion methods, i.e., processing dealing with data and information from multiple sources to achieve improved information for decision making can be grouped into three main categories: -pixel-level: the pixel values of the sources to be fused are jointly processed [6][7][8][9]; -feature-level: features like lines, regions, keypoints, maps, and so on, are first extracted independently from each source image and subsequently combined to produce higher-level cross-source features, which may represent the desired output or be further processed [10][11][12][13][14][15][16][17]; -decision-level: the high-level information extracted independently from each source is combined to provide the final outcome, for example using fuzzy logic [18,19], decision trees [20], Bayesian inference [21], Dempster-Shafer theory [22], and so forth.…”