Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network  

作　　者：Hui Chen Yue’an Qiu Dameng Yin Jin Chen Xuehong Chen Shuaijun Liu Licong Liu 

机构地区：[1]State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China [2]Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Beijing 100081,China

出　　处：《The Crop Journal》2022年第5期1460-1469,共10页作物学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (67441830108 and 41871224)。

摘　　要：Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop cla

关 键 词：Crop classification Convolutional neural network Handcrafted feature Stacked spectral feature space patch Spectral information 

分 类 号：S127[农业科学—农业基础科学]