## 4 Discussion  
### Cloud and Haze  
Smoke, clouds, and haze have very similar morphologies and spectral properties, so clouds and haze will affect the recognition results of smoke [51]. Moreover, with thick cloud cover, the information about smoke is completely lost. As a result, it is difficult to identify smoke accurately in satellite imagery [51].  
To date, many scholars have performed extensive studies on de-clouding and dehazing of optical remote sensing images and have proposed several solutions. Using a wavelet transform, [PERSON] et al. [52] removed thin clouds from panchromatic remote sensing images. [PERSON] and [PERSON] [53] proposed a method for removing clouds using the HSI color space. [PERSON] et al. [54] created a method for cloud removal using single-scene remote sensing images. [PERSON] et al. [55] proposed a homomorphic filtering method for removing thin clouds. Although these traditional methods for removing clouds from optical remote sensing images have delivered satisfactory results, some of the methods for processing thin and thick clouds cannot be used universally, as they rely on prior knowledge. the feature information needs to be manually extracted, and the restored image details are inaccurate. Therefore, it is not feasible to use these traditional methods for cloud removal on a widespread basis.  
With the development of machine learning, a support vector machine method was proposed by [PERSON] et al. [56] that removes thick clouds and cloud shadows from remote sensing images. As compared to traditional methods, the method can better restore information about an object on the ground obscured by thick cloud. Deep learning has led to major breakthroughs in computer vision in recent years. In 2020, [PERSON] et al. [51] provided a solution for removing clouds from optical remote sensing images used an improved conditional generative adversarial network (CGAN). A spatial pooling layer was introduced into the generator of the original CGAN, and regression loss was added, which improved the generation. In their experiments, this method achieved better results when removing cloud and in maintaining the fidelity of the images.  
Due to the problems of low contrast and blurred details in foggy images, [PERSON] et al. [57] decomposed an image into textural and structural layers, and dehazed the structural layers containing most of the fog. [PERSON] et al. [58] provided an image dehazing method using the dark channel prior model, which is simple and effective but overestimates the density of the fog and distorts colors. With the development of deep learning, [PERSON] et al. [59] used a convolutional neural network (CNN) to estimate the transmittance of haze. A model of atmospheric scattering was used to restore a haze-free image. Owing to its overreliance on atmospheric scattering, this indirect dehazing method can sometimes overestimate the transmittance despite achieving good results. The traditional method for dehazing an image relies on models of atmospheric scattering, which result in color distortions and insufficient dehazing. Using a residual attention mechanism, [PERSON] et al. [60] proposed an end-to-end dehazing algorithm that effectively reduced the color distortion and the dehazing was less incomplete.  
There are many other de-clouding and dehazing methods. Traditional and deep learning methods have their own advantages and disadvantages. Methods for removing cloud and fog from remote sensing images are very mature, and the image quality after cloud and fog removal is continually increasing. In the following research, we can refer to the above-mentioned algorithms for de-clouding and de-hazing. In addition, we can utilize cloud and haze removal operations on the acquired remote sensing data in order to prevent clouds and haze from interfering with smoke detection and improve the model's performance.