Recently, several land cover classification models have achieved great success in terms of both accuracy and computational performance. However, it remains challenging due to inter-class similarities, intra-class variations, scale-related inaccuracies, and high computational complexity. First, these methods fail to establish a correlation among different feature maps during multiscale feature extraction, leading to inter-class similarities and intra-class variations. Second, they under-utilize feature interdependencies of the context contained in each layer of the encoder-decoder architecture, causing scale-related inaccuracies. Third, they cause checkerboard artifacts and blurry edges, which can negatively impact the accuracy and generated segmentation map at increased computational cost. To address these problems, this article proposes a novel multiscale context-aware feature fusion network (MCN) for high-resolution urban scene images. MCN mainly consists of three modules: 1) multiscale feature enhancement module (MFE) for backbone network to extract rich spatial information dynamically by incorporating dense correlation among feature maps with different receptive fields, 2) multilayer feature fusion module (MLF) as skip connections to produce a single high-level representation of the local-global context by capturing low-, mid-, high- level interdependencies at different encoder-decoder stages, and 3) pixel shuffle decoder (PSD) to reduce the blurry edges and checkerboard artifacts while upsampling with reduced number of parameters. Experiments on three high-resolution aerial and satellite urban scene datasets show that MCN consistently outperforms the mainstream land cover classification models. Specifically, MCN achieves an OA of 93.51 on Potsdam, 90.18 on Vaihingen, and a mIoU of 73.73 on DeepGlobe.

It is a big challenge to quickly and accurately recognize targets in a complex background. The mutual constraints between a wide field of vision (FOV) and high resolution affect the optical tracking and imaging ability in a wide area. In nature, raptors possess unique imaging structures and optic nerve systems that can accurately recognize prey. This paper proposes an imaging system combined with a deep learning algorithm based on the visual characteristics of raptors, aiming to achieve wide FOV, high spatial resolution, and accurate recognition ability. As for the imaging system, two sub-optical systems with different focal lengths and various-size photoreceptor cells jointly simulate the deep fovea of a raptor’s eye. The one simulating the peripheral region has a wide FOV and high sensitivity for capturing the target quickly by means of short focal length and large-size photoreceptor cells, and the other imitating the central region has high resolution for recognizing the target accurately through the long focal length and small-size photoreceptor cells. Furthermore, the proposed algorithm with an attention and feedback network based on octave convolution (AOCNet) simulates the mechanism of the optic nerve pathway by adding it into the convolutional neural network (CNN), thereby enhancing the ability of feature extraction and target recognition. Experimental results show that the target imaging and recognition system eliminates the limitation between wide FOV and high spatial resolution, and effectively improves the accuracy of target recognition in complex backgrounds.