Abstract
Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.
Highlights
The goal of this section is to introduce a new deep neural model for remote sensing data processing, aimed at conducting classification of hyperspectral images (HSIs)
This section will highlight the use of deep learning (DL) strategies for data analysis, highlighting those methods based on convolutional neural networks (CNNs) as the current-state-of-the-art of DL field
As we can observe from Equation (1), a standard artificial neural networks (ANNs) with L fully connected layers needs to learn ∑lL=−11 N(l) · N(l+1) different and independent weights, which in practice is extremely inefficient in deep architectures for HSI classification, due the large number of parameters that must be fine-tuned
Summary
The goal of this section is to introduce a new deep neural model for remote sensing data processing, aimed at conducting classification of hyperspectral images (HSIs). Indian Pines (IP): It was gathered by the AVIRIS sensor [2] in Northwestern Indiana (United States), capturing a set of agricultural fields This HSI scene contains 145 × 145 pixels of 224 spectral bands in the wavelength range 0.4–2.45 μm, with spectral and spatial resolution of 0.01 μm and 20 m per pixel (mpp). University of Pavia (UP): It was captured by the ROSIS sensor [3] University of Pavia campus, located in northern Italy In this case, the HSI dataset comprises 610 × 340 pixels with 103 spectral bands, after discarding certain noisy bands. The available ground-truth information comprises about 20% of the pixels (42,776 of 207,400), labeled into 9 different classes
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