摘要
利用遥感影像数据进行土地利用/覆被分类是多学科共同关注的热点问题,但传统自动分类方法仍然难以满足应用需求,以隐狄利克雷分配模型(latent dirichlet allocation,LDA)为代表的概率主题模型能够建立底层特征和高层语义之间的桥梁,近年来也被引入了遥感影像分析领域,但多集中于针对高空间分辨遥感影像的分析。该文分析了一般概率主题模型在遥感影像空间分辨率降低后面临的问题,在此基础上借鉴词对主题模型(biterm topic model,BTM)对单词稀疏文档的推理能力,将其引入中空间分辨率遥感影像的分类中,并提出使用空间相邻的视觉单词对作为模型的观测数据。试验结果表明,BTM模型的分类性能优于LDA模型,并且使用空间相邻视觉单词对可以比标准BTM模型使用更少的观测数据,取得更高的分类精度。
Land Use/Land Cover type automatic interpretation based on remote sensing data is one of the key problems in many relevant fields. Although a large number of image classification algorithms have been developed, most of them can hardly meet the application requirements. Probabilistic topic models, represented by Latent Dirichlet Allocation （LDA） model, have showed a great success in the field of natural language processing and image processing, which can be used to effectively overcome the gap between low-level features and high-level semantic. In recent years it have also been introduced into remote sensing image analysis field, while most of the researches focused on the analysis of high-resolution remote sensing images. Nonetheless, the moderate-resolution remote sensing data is one of the main sources in Land Use/Land Cover type automatic interpretation. The study analyzed the problem faced by traditional probabilistic topic models in reduced resolution remote sensing image analyzing, and pointed out that low segmentation scale made the image objects small and contained fewer pixels. In fact the objects, which are regarded as image documents in current work, are sparse in moderate resolution remote sensing image. The scarcity led to poor stability when using the standard LDA model to infer the semantic of short documents. So Biterm Topic Model （BTM） showed the ability of inferring the semantic of sparse documents. BTM learns topics by directly modeling the generation of word co-occurrence patterns in the corpus, making the inference effective with the rich corpus-level information. By segmenting the remote sensing image into two scales and regarding the image objects at two levels as short documents and visual words respectively, BTM was introduced to the classification of moderate resolution remote sensing image. The co-occurrence of words denoted as biterm in a document were modeled in BTM extracted by setting a short context refers to a small, fixed-size window over a term sequence. However, the seq
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第22期259-265,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(41501431
41601449)
江苏省高校自然科学研究面上项目资助(15KJB420001
16KJD420002)
关键词
土地利用
遥感
模型
概率主题模型
中空间分辨率
遥感影像分类
词对主题模型
land use
remote sensing
models
probabilistic topic model
moderate resolution
remote sensing imageclassification
biterm topic model
