Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often have problems with accurate feature extraction. In this paper, based on remote sensing images acquired by the Gaofen-2 satellite, we proposed a Modified DeepLabv3+ model to detect black and odorous water. To reduce the complexity of the encoder part of the model, Modified Deeplabv3+ incorporates a lightweight MobileNetV2 network. A convolutional attention module was introduced to improve the focus on the features of black and odorous water. Then, a fuzzy block was crafted to reduce the uncertainty of the raw data. Additionally, a new loss function was formulated to solve the problem of category imbalance. A series of experiments were conducted on both remote sensing images for the black and odorous water detection (RSBD) dataset and the water pollution dataset, demonstrating that the Modified DeepLabv3+ model outperforms other commonly used semantic segmentation networks. It effectively captures detailed information and reduces image segmentation errors. In addition, in order to better identify black and odorous water and enrich the spectral information of the image, we have generated derived bands using the black and odorous water index. These derived bands were fused together with the original image to construct the RSBD-II dataset. The experimental results show that adding a black and odorous water feature index can achieve a better detection effect.