# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 1 Introduction  
All countries are paying great attention to urban land planning, agricultural management, and marine environmental monitoring, which are related to economic and military development. Additionally, they also have significant impacts on the ecological environment of the earth [1; 2; 3]. Thus, earth observation technology is of vital importance. Remote sensing (RS) can dramatically increase the scope and quality of earth observation, which provides a certain degree of convenience for researchers to observe the characteristics of objects on the earth with a high resolution. In the field of RS, extracting high-precision objects from optical and synthetic aperture radar (SAR) images has become an enormous challenge and has attracted widespread attention. Benefiting from the advancement of neural networks, increasing numbers of researchers are applying deep learning methods to RS image processing [4; 5]. In general, RS image processing can be divided into several tasks. The first is an image-level task, where different objects are categorized into their respective classes based on their attributes andfeatures [6; 7]. The second task is an object-level task, which locates the specific position of each object in the picture and separates the region of interest (RoI) from the background region using bounding boxes [8; 9; 10]. Finally, in the pixel-level task, semantic-segmentation-based methods separate the background from predicted objects on a pixel-by-pixel basis, but they are unable to distinguish different objects within the same category [11; 12]. Object-detection and semantic-segmentation methods are also utilized to interpret images in RS image processing [13; 14; 15]. By performing instance-level localization and pixel-level classification of objects of interest simultaneously, instance segmentation (IS) methods can combine the benefits of object-level methods and semantic-segmentation methods to interpret more comprehensive information. Due to the dense and compact distribution of objects in SAR and optical images, we adopt IS to interpret objects at the pixel-level and distinguish individual objects within the same category. IS can be divided into one-stage-segmentation methods [16; 17; 18] and two-stage-segmentation methods [19; 20; 21]. One-stage IS algorithms directly extract features from the input image and output results in a single forward inference process, which typically results in a faster inference speed. Although the one-stage method has good performance in real-time detection, it is still slightly insufficient for the high-precision segmentation requirements of high-resolution RS images. On the other hand, two-stage IS algorithms are suitable for scenarios that require a high segmentation accuracy because they can locate and segment objects more accurately. However, their processing speed tends to be relatively slow. The mask region-based convolutional neural network (Mask-RCNN) [21] is the most representative algorithm among two-stage-segmentation paradigms. On the basis of the Faster-RCNN [9], a fully convolutional network (FCN) [11] is connected in parallel to the segmentation head (the head usually refers to the network layer that predicts the output in deep learning) obtained by the mask instance. It often serves as a baseline in numerous IS comparative experiments. Owing to its excellent segmentation performance, there are also many IS algorithms in the field of RS that are improved on it [22; 23; 24] and have achieved good segmentation results. The choice of the method depends on the specific application scenario and the desired level of accuracy and speed for IS. Recent years have seen numerous innovations in IS using RS images. The cascade network was first proposed by [PERSON] et al. [25] and is extensively used by researchers in detection and segmentation tasks [19; 20]. The structure consists of multiple stages, and the output information of each level is passed to the next level as input information, realizing the level-by-level transfer and processing of information.