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 select...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 classification in precision agriculture.展开更多
A large number of publications have incorporated deep learning in the process of remote sensing change detection.In these Deep Learning Change Detection(DLCD)publications,deep learning methods have demonstrated their ...A large number of publications have incorporated deep learning in the process of remote sensing change detection.In these Deep Learning Change Detection(DLCD)publications,deep learning methods have demonstrated their superiority over conventional change detection methods.However,the theoretical underpinnings of why deep learning improves the performance of change detection remain unresolved.As of today,few in-depth reviews have investigated the mechanisms of DLCD.Without such a review,five critical questions remain unclear.Does DLCD provide improved information representation for change detection?If so,how?How to select an appropriate DLCD method and why?How much does each type of change benefits from DLCD in terms of its performance?What are the major limitations of existing DLCD methods and what are the prospects for DLCD?To address these five questions,we reviewed according to the following strategies.We grouped the DLCD information assemblages into the four unique dimensions of remote sensing:spectral,spatial,temporal,and multi-sensor.For the extraction of information in each dimension,the difference between DLCD and conventional change detection methods was compared.We proposed a taxonomy of existing DLCD methods by dividing them into two distinctive pools:separate and coupled models.Their advantages,limitations,applicability,and performance were thoroughly investigated and explicitly presented.We examined the variations in performance between DLCD and conventional change detection.We depicted two limitations of DLCD,i.e.training sample and hardware and software dilemmas.Based on these analyses,we identified directions for future developments.As a result of our review,we found that DLCD’s advantages over conventional change detection can be attributed to three factors:improved information representation;improved change detection methods;and performance enhancements.DLCD has to surpass the limitations with regard to training samples and computing infrastructure.We envision this review can boost developments of deep learning in change detection applications.展开更多
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘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 classification in precision agriculture.
文摘A large number of publications have incorporated deep learning in the process of remote sensing change detection.In these Deep Learning Change Detection(DLCD)publications,deep learning methods have demonstrated their superiority over conventional change detection methods.However,the theoretical underpinnings of why deep learning improves the performance of change detection remain unresolved.As of today,few in-depth reviews have investigated the mechanisms of DLCD.Without such a review,five critical questions remain unclear.Does DLCD provide improved information representation for change detection?If so,how?How to select an appropriate DLCD method and why?How much does each type of change benefits from DLCD in terms of its performance?What are the major limitations of existing DLCD methods and what are the prospects for DLCD?To address these five questions,we reviewed according to the following strategies.We grouped the DLCD information assemblages into the four unique dimensions of remote sensing:spectral,spatial,temporal,and multi-sensor.For the extraction of information in each dimension,the difference between DLCD and conventional change detection methods was compared.We proposed a taxonomy of existing DLCD methods by dividing them into two distinctive pools:separate and coupled models.Their advantages,limitations,applicability,and performance were thoroughly investigated and explicitly presented.We examined the variations in performance between DLCD and conventional change detection.We depicted two limitations of DLCD,i.e.training sample and hardware and software dilemmas.Based on these analyses,we identified directions for future developments.As a result of our review,we found that DLCD’s advantages over conventional change detection can be attributed to three factors:improved information representation;improved change detection methods;and performance enhancements.DLCD has to surpass the limitations with regard to training samples and computing infrastructure.We envision this review can boost developments of deep learning in change detection applications.
