Oil spill incidents threaten the marine ecological environment. Detecting sea surface oil slicks by remote sensing images provides support for the efficient treatment of oil spills. This is important for sustainable marine development. However, traditional methods based on field analysis are time-consuming. Spectral indices lack applicability. In addition, traditional machine learning methods strictly rely on training and testing samples which are in short supply in oil spill images. Inspired by the spectral DNA encoding method, a spectral gene extraction (SGE) method was proposed to detect oil spills in hyperspectral images (HSI) and multispectral images (MSI). The SGE method contained a parameter and two strategies. The parameter of elimination was designed based on the population genetic frequency. It was used to control the number of spectral genes. The spectral gene extraction strategies, named largest in-class similarity (LIS) strategy and largest inter-class difference (LID) strategy, were proposed to mine the spectral genes by oil spill samples. The oil spills would be determined by calculating the similarity of the extracted spectral genes to the DNA encoded images. In this research, the SGE method was validated by two AVIRIS images of the Gulf of Mexico oil spill, one MODIS image of the Gulf of Mexico oil spill, and one Landsat 8 image of a Persian Gulf oil spill. The oil spills in different remote sensing images could be detected accurately by the proposed method in a small set of samples. Experimental results indicated that the proposed method was suitable for detecting marine oil spills in AVIRIS, MODIS, and Landsat 8 images. In addition, the SGE method with the LIS strategy was more suitable for detecting oil spills in HSI. Its proper elimination rates were 0.8~1.0. The SGE method with the LID strategy was more suitable for detecting oil spills in MSI. Its proper elimination rates were 0.5~0.7.

Oil spill incidents endanger the marine environment. A spaceborne multispectral imager can be used to observe the floating oil slicks on a large scale. However, traditional methods are only suitable for small-scale detection situations. It is difficult to distinguish oil slicks from the heterogeneous marine environment in large area images because the spectral information on oil slicks and seawater are analogous and mutative. We suppose that finding out the sensitivities of spectral indices to seawater environment contributes to avoiding identifying seawater as oil slick look-alikes. Based on this assumption, this research conducted experiments to test several proposed oil slick detection indices: fluorescence index (FI), rotation-absorption index (RAI), shortwave infrared (SWIR) reflectance, chlorophyll (CHL) spectral characteristic, and colored dissolved organic matter (CDOM) spectral characteristic. The experimental results indicated that the indices were sensitive to different marine environments and combining the complementary indices could extract oil slicks from the heterogeneous marine environment accurately.

Remote sensing technologies are suitable for detecting marine oil-gas leakages on a large scale. It is important to structure an accurate method for detecting marine oil-gas leakages in varied remote sensing images. However, traditional spectral indexes have limited applicability. Machine learning methods need plenty of training and testing samples to establish the optimized models, which is too rigorous for satellite images. Thus, we proposed a multi-scale encoding (MSE) method with spectral shape information (SSI) to detect the oil-gas leakages in multi-source remote sensing data. First, the spectral amplitude information (SAI) and SSI of the original spectra were encoded into a series of code words according to the scales. Then, the differential code words of the marine oil-gas leakage objects were extracted from the SAI and SSI code words. Finally, the pixels of the encoded hyperspectral image (HSI) and multispectral image (MSI) would be determined by the differential code words. Seven images captured by different platforms/sensors (Landsat 7, Landsat 8, MODIS, Sentinel 2, Zhuhai-1, and AVIRIS) were used to validate the performance of the proposed method. The experimental results indicated that the MSE method with SSI was convergent and could detect the oil-gas leakages accurately in different images using a small set of samples.

Spectral encoding is an important way of describing spectral features and patterns. Traditional methods focused on encoding the spectral amplitude information (SAI). Abundant spectral shape information (SSI) was wasted. In addition, traditional statistical encoding methods might only gain local adaptability since different objects should have their own best encoding scales. In order to obtain differential signals from hyperspectral images (HSI) for detecting ground objects correctly, a multi-scale encoding (MSE) method with SSI and two optimization strategies were proposed in this research. The proposed method concentrated on describing the SAI and SSI of the spectral reflectance signals. Four widely used open data sets were adopted to validate the performance of the proposed method. Experimental results indicated that the MSE method with SSI could describe the details of spectral signals accurately. It could obtain excellent performance for detecting similar objects with a small number of samples. In addition, the optimization strategies contributed to obtaining the best result from dynamic encoding scales.

In recent years, the wars have gradually increased the risk of marine oil spill accidents. Marine oil spill monitoring becomes more and more important for preventing marine oil pollutions. Chinese Zhuhai-1 satellite can capture abundant spectral reflectance signals. It is a significant way of detecting marine oil spills. Most of the traditional oil spill detection methods only used a small amount of spectral information. It made it difficult identify oil spills accurately from the inhomogeneous marine environment. In order to mine the key differential spectral information of oil slicks, inspired by the encoding method of spectral DNA, an advanced spectral DNA encoding (ASDE) strategy was proposed to describe the spectral details in Zhuhai-1 images. On this basis, two kinds of key spectral information extraction methods were proposed to mine the spectral genes of oil slicks. Finally, the extracted spectral genes were used to detect the marine oil spills. Three Zhuhai-1 satellite images were used to validate the performance of the proposed method based on ASDE strategy. The experimental results indicated that the proposed method could precisely describe the spectral differences of oil slicks and sea-water in Zhuhai-1 images. In addition, the extracted spectral genes could detect marine oil spills correctly.

In recent years, wars have gradually increased the risk of marine oil spill accidents. Marine oil spill monitoring becomes more and more important for preventing marine oil pollution. The Chinese Zhuhai-1 satellite can capture abundant spectral reflectance signals. It is a significant way of detecting marine oil spills. Most of the traditional oil spill detection methods only used a small amount of spectral information. It made it difficult identify oil spills accurately from the inhomogeneous marine environment. In order to mine the key differential spectral information of oil slicks, inspired by the encoding method of spectral DNA, an advanced spectral DNA encoding (ASDE) strategy was proposed to describe the spectral details in Zhuhai-1 images. On this basis, two kinds of key spectral information extraction methods were proposed to mine the spectral genes of oil slicks. Finally, the extracted spectral genes were used to detect the marine oil spills. Three Zhuhai-1 satellite images were used to validate the performance of the proposed method based on ASDE strategy. The experimental results indicated that the proposed method could precisely describe the spectral differences in oil slicks and seawater in Zhuhai-1 images. In addition, the extracted spectral genes could detect marine oil spills correctly.