Land cover classification is a multi-class segmentation task to classify each pixel into a certain natural or man-made category of the earth surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by down sampling or cropping them into small patches less than 512*512 pixels before sending them to a deep neural network. However, down sampling images incurs spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of years of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs parallel and shallow architecture to boost inference speed and friendly support image patches more than 10X larger. To enhance boundary and small segments discrimination, we also propose a method that captures category impurity areas, exploits boundary information and exerts an extra penalty on boundaries and small segment misjudgment. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet acquires state-of-the-art accuracy on two land-cover classification datasets and infers 2X faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications.
翻译:土地覆盖分类是一项多级分类任务,将每个像素分为水、土壤、自然植被、作物和人类基础设施等特定自然或人为的地球表面类别,将每个像素分为水、土壤、自然植被、作物和人类基础设施等特定自然或人为类别。由于硬件计算资源和记忆能力的限制,大多数现有研究通过下取样处理原始遥感图像,或将图像植入小片小片小片小片小片小小片小片小片小片小到深神经网络之前,先将图像加工成512*512512像素小片小片小片小片小。然而,下取样图像造成空间详细损失,使小片小片小部分难以区分,并扭转数十年来在空间分辨率上取得的更快的空间分辨率进展。将图像切入小片小块导致丢失长距离背景信息,将预测结果恢复到原有大小。为了应对上述弱点,我们提出了一个高效的轻量的语系分布分网形图分网化网络的特征特征,同时和平均处理不同程度的地面部分,并使用平行和浅层建筑来提高速度和友好支持图像应用。