It seems difficult to recognize an object from its background with similar color using conventional segmentation methods. An efficient way is to utilize hyperspectral images that contain more wave bands and richer information than only RGB components. Particularly in our task, we aim to separate a pepper from densely packed green leaves for automatic picking in agriculture. Given that hyperspectral imaging can be regarded as a kind of wave propagation process, we make a novel attempt of introducing a complex neural network tailored for wave-related problems. Due to the lack of hyperspectral data, pixelwise training is deployed, and 1D fast Fourier transform of the hyperspectral data is used for the construction of complex input. Experimental results have showcased that a complex neural network outperforms a real-valued one in terms of detection accuracy by 3.9% and F1 score by 1.33%. Moreover, it enables the ability to select frequency bands used such as low-frequency components to boost performance as well as prevent overfitting problems for learning more generalization features. Thus, we put forward a lightweight pixelwise complex model for hyperspectral-related problems and provide an efficient way for green pepper automatic picking in agriculture using small datasets.

In cognitive science, the bottleneck of information processing capabilities promotes humans to selectively focus on part of the visible information while ignoring the rest. This is usually called the attention mechanism. Among different attention mechanisms, spectrum attention focuses on assigning every frequency channel obtained by DCT an appropriate weight to achieve adaptive filtering and sends the reweighting frequency components into a real-valued neural network. However, DCT is a real-valued transformation for signal processing thus phase information tends to be omitted. Here we consider a departure from these conventions and propose a spectrum attention complex neural network assisted by DFT, to make a holistic frequency analysis in a completely complex domain. Our proposed network demonstrates outstanding performance on a one-dimensional hyperspectral dataset and MNIST dataset, compared to both traditional complex neural networks and DCT based frequency attention methods.

Detecting and identifying objects of similar color is a challenging task in computer vision. Green peppers in a natural environment can be found using the abundant information provided by a hyperspectral camera in the spectral domain, but the hyperspectral camera is an expensive device. Therefore, we propose a novel framework called Optical Filter Net, which enables the design of an optical filter that improves the performance of green pepper segmentation by a specific red-green-blue (RGB) camera system. When installed with the optical filter, the system can efficiently utilize the spectral information in the visible wavelength to distinguish green pepper and foliage without requiring an expensive hyperspectral camera. A main finding is the similarity between the transmission curve of the optical filter and the depth-wise convolution kernel without bias. Accordingly, we can treat the transmission curve of the optical filter as one layer of a deep neural network. The whole structure can be trained in an end-to-end manner. To comply with the physical requirement of the optical filter, we further constrain the training process to achieve a non-negative and smooth transmission curve. In an experimental evaluation on our dataset, our proposed spectral-aware RGB camera system outperformed the RGB camera system without an optical filter.