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《HybridSN: Exploring 3-D–2-DCNN Feature Hierarchy for Hyperspectral Image Classification》论文学习笔记

作者:互联网

文章目录

论文基本信息

研究背景

高光谱图像分类广泛应用于遥感图像的分析。高光谱图像包括不同波段的图像。卷积神经网络(CNN)是一种常用的基于深度学习的视觉数据处理方法。CNN用于HSI分类的使用在最近的文章中也可以看到。这些方法大多基于2D CNN,然而,HSI分类性能高度依赖于空间和光谱信息。由于计算复杂度的增加,很少有方法利用3D CNN。本文提出了一种用于HSI分类的混合光谱卷积神经网络(HybridSN)。基本上,HybridSN是一个光谱-空间 3D-CNN,然后是空间 2D-CNN。3D-CNN促进了从光谱波段堆栈的联合空间-光谱特征表示。在3D-CNN之上的2D-CNN进一步学习了更抽象的层次空间表示。此外,使用混合CNN比单独使用3D-CNN降低了模型的复杂性。

读完摘要后的疑问

读完文章后对上述问题的回答

高光谱基础知识

HybridSN

根据上边的描述,2D的CNN很难充分地提取高光谱的空间信息,而3D的CNN则是由于计算量庞大而很少被使用。
本文将2D和3D的CNN进行了结合,使用3D的CNN高光谱的空间特征的提取,使用2D的CNN进行更抽象的层次空间表示。
在这里插入图片描述
网络结构如上图所示(3个三维卷积,1个二维卷积,3个全连接层):

实验验证

数据集描述

三种开源高光谱图像数据集,Indian Pines(IP), University of Pavia(UP) and Salinas Scene(SA)

IP图像空间维度为:145×145,波长范围为400-2500nm,共有224个光谱波段
UP图像空间维度为:610×340,波长范围430-860nm,共有103个光谱波段
SA图像空间维度为:512×217,波长范围360-2500nm,共有224个光谱波段

分类结果

使用了Overall Accuracy (OA)、Average Accuracy (AA)和Kappa Coefficient (Kappa)评价指标来判断HSI分类性能。其中,OA表示总体测试样本中正确分类的样本数;AA表示分类精度的平均值;Kappa是一种统计测量的度量,它提供了关于地面真相图和分类图之间的强一致性的相互信息。将所提出的HybridSN模型的结果与最广泛使用的监督方法如SVM[33]、2D-CNN[34]、3D-CNN[35]、M3D-CNN[36]和SSRN[27]进行比较。30%和70%的数据分别随机分为训练组和测试组2。我们使用了比较方法的公开代码3来计算结果。
在这里插入图片描述
在这里插入图片描述
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从表二可以看出,混合算法在保持最小标准偏差的同时,在每个数据集上都优于所有的比较方法。
图3显示了混合网络在IP、UP和SA数据集上的HSI分类性能的混淆矩阵。
训练集和验证集100个epoch的准确性和损失收敛如图4所示。可以看出,收敛时间约为50个epoch,这表明作者的方法收敛速度较快。
混合动力网络模型的计算效率体现在训练和测试次数方面,如表3所示。

代码分析

三维卷积部分:
conv1:(1, 30, 25, 25), 8个 7x3x3 的卷积核 ==>(8, 24, 23, 23)
conv2:(8, 24, 23, 23), 16个 5x3x3 的卷积核 ==>(16, 20, 21, 21)
conv3:(16, 20, 21, 21),32个 3x3x3 的卷积核 ==>(32, 18, 19, 19)
接下来要进行二维卷积,因此把前面的 32*18 reshape 一下,得到 (576, 19, 19)
二维卷积部分:
(576, 19, 19) 64个 3x3 的卷积核,得到 (64, 17, 17)
接下来是一个 flatten 操作,变为 18496 维的向量,
接下来依次为256,128节点的全连接层,都使用比例为0.4的 Dropout,
最后输出为 16 个节点,是最终的分类类别数。

! wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat
! wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat
! pip install spectral
#获取数据,并导入基本库
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
import spectral
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

定义HybridSN 类:

class_num = 16

class HybridSN(nn.Module):
  ''' your code here '''
  def __init__(self):
    super(HybridSN, self).__init__()
    self.conv3d_1 = nn.Sequential(
        nn.Conv3d(1, 8, kernel_size=(7, 3, 3), stride=1, padding=0),
        nn.BatchNorm3d(8),
        nn.ReLU(inplace = True),
    )
    self.conv3d_2 = nn.Sequential(
        nn.Conv3d(8, 16, kernel_size=(5, 3, 3), stride=1, padding=0),
        nn.BatchNorm3d(16),
        nn.ReLU(inplace = True),
    ) 
    self.conv3d_3 = nn.Sequential(
        nn.Conv3d(16, 32, kernel_size=(3, 3, 3), stride=1, padding=0),
        nn.BatchNorm3d(32),
        nn.ReLU(inplace = True)
    )

    self.conv2d_4 = nn.Sequential(
        nn.Conv2d(576, 64, kernel_size=(3, 3), stride=1, padding=0),
        nn.BatchNorm2d(64),
        nn.ReLU(inplace = True),
    )
    self.fc1 = nn.Linear(18496,256)
    self.fc2 = nn.Linear(256,128)
    self.fc3 = nn.Linear(128,16)
    self.dropout = nn.Dropout(p = 0.4)

  def forward(self,x):
    out = self.conv3d_1(x)
    out = self.conv3d_2(out)
    out = self.conv3d_3(out)
    out = self.conv2d_4(out.reshape(out.shape[0],-1,19,19))
    out = out.reshape(out.shape[0],-1)
    out = F.relu(self.dropout(self.fc1(out)))
    out = F.relu(self.dropout(self.fc2(out)))
    out = self.fc3(out)
    return out
# 随机输入,测试网络结构是否通
# x = torch.randn(1, 1, 30, 25, 25)
# net = HybridSN()
# y = net(x)
# print(y.shape)

创建数据集
首先对高光谱数据实施PCA降维;然后创建 keras 方便处理的数据格式;然后随机抽取 10% 数据做为训练集,剩余的做为测试集。

首先定义基本函数:

# 对高光谱数据 X 应用 PCA 变换
def applyPCA(X, numComponents):
    newX = np.reshape(X, (-1, X.shape[2]))
    pca = PCA(n_components=numComponents, whiten=True)
    newX = pca.fit_transform(newX)
    newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents))
    return newX

# 对单个像素周围提取 patch 时,边缘像素就无法取了,因此,给这部分像素进行 padding 操作
def padWithZeros(X, margin=2):
    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))
    x_offset = margin
    y_offset = margin
    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X
    return newX

# 在每个像素周围提取 patch ,然后创建成符合 keras 处理的格式
def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):
    # 给 X 做 padding
    margin = int((windowSize - 1) / 2)
    zeroPaddedX = padWithZeros(X, margin=margin)
    # split patches
    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))
    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))
    patchIndex = 0
    for r in range(margin, zeroPaddedX.shape[0] - margin):
        for c in range(margin, zeroPaddedX.shape[1] - margin):
            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   
            patchesData[patchIndex, :, :, :] = patch
            patchesLabels[patchIndex] = y[r-margin, c-margin]
            patchIndex = patchIndex + 1
    if removeZeroLabels:
        patchesData = patchesData[patchesLabels>0,:,:,:]
        patchesLabels = patchesLabels[patchesLabels>0]
        patchesLabels -= 1
    return patchesData, patchesLabels

def splitTrainTestSet(X, y, testRatio, randomState=345):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)
    return X_train, X_test, y_train, y_test

下面读取并创建数据集:

# 地物类别
class_num = 16
X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']
y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']

# 用于测试样本的比例
test_ratio = 0.90
# 每个像素周围提取 patch 的尺寸
patch_size = 25
# 使用 PCA 降维,得到主成分的数量
pca_components = 30

print('Hyperspectral data shape: ', X.shape)
print('Label shape: ', y.shape)

print('\n... ... PCA tranformation ... ...')
X_pca = applyPCA(X, numComponents=pca_components)
print('Data shape after PCA: ', X_pca.shape)

print('\n... ... create data cubes ... ...')
X_pca, y = createImageCubes(X_pca, y, windowSize=patch_size)
print('Data cube X shape: ', X_pca.shape)
print('Data cube y shape: ', y.shape)

print('\n... ... create train & test data ... ...')
Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X_pca, y, test_ratio)
print('Xtrain shape: ', Xtrain.shape)
print('Xtest  shape: ', Xtest.shape)

# 改变 Xtrain, Ytrain 的形状,以符合 keras 的要求
Xtrain = Xtrain.reshape(-1, patch_size, patch_size, pca_components, 1)
Xtest  = Xtest.reshape(-1, patch_size, patch_size, pca_components, 1)
print('before transpose: Xtrain shape: ', Xtrain.shape) 
print('before transpose: Xtest  shape: ', Xtest.shape) 

# 为了适应 pytorch 结构,数据要做 transpose
Xtrain = Xtrain.transpose(0, 4, 3, 1, 2)
Xtest  = Xtest.transpose(0, 4, 3, 1, 2)
print('after transpose: Xtrain shape: ', Xtrain.shape) 
print('after transpose: Xtest  shape: ', Xtest.shape) 


""" Training dataset"""
class TrainDS(torch.utils.data.Dataset): 
    def __init__(self):
        self.len = Xtrain.shape[0]
        self.x_data = torch.FloatTensor(Xtrain)
        self.y_data = torch.LongTensor(ytrain)        
    def __getitem__(self, index):
        # 根据索引返回数据和对应的标签
        return self.x_data[index], self.y_data[index]
    def __len__(self): 
        # 返回文件数据的数目
        return self.len

""" Testing dataset"""
class TestDS(torch.utils.data.Dataset): 
    def __init__(self):
        self.len = Xtest.shape[0]
        self.x_data = torch.FloatTensor(Xtest)
        self.y_data = torch.LongTensor(ytest)
    def __getitem__(self, index):
        # 根据索引返回数据和对应的标签
        return self.x_data[index], self.y_data[index]
    def __len__(self): 
        # 返回文件数据的数目
        return self.len

# 创建 trainloader 和 testloader
trainset = TrainDS()
testset  = TestDS()
train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)
test_loader  = torch.utils.data.DataLoader(dataset=testset,  batch_size=128, shuffle=False, num_workers=2)

在这里插入图片描述
开始训练
这里使用cpu训练,只跑了50个epoch

# 使用GPU训练,可以在菜单 "代码执行工具" -> "更改运行时类型" 里进行设置
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 网络放到GPU上
net = HybridSN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 开始训练
total_loss = 0
for epoch in range(50):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        # 优化器梯度归零
        optimizer.zero_grad()
        # 正向传播 + 反向传播 + 优化 
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print('[Epoch: %d]   [loss avg: %.4f]   [current loss: %.4f]' %(epoch + 1, total_loss/(epoch+1), loss.item()))

print('Finished Training')

模型测试

count = 0
# 模型测试
for inputs, _ in test_loader:
    inputs = inputs.to(device)
    outputs = net(inputs)
    outputs = np.argmax(outputs.detach().cpu().numpy(), axis=1)
    if count == 0:
        y_pred_test =  outputs
        count = 1
    else:
        y_pred_test = np.concatenate( (y_pred_test, outputs) )

# 生成分类报告
classification = classification_report(ytest, y_pred_test, digits=4)
print(classification)

在这里插入图片描述
备用函数¶
下面是用于计算各个类准确率,显示结果的备用函数,以供参考

from operator import truediv

def AA_andEachClassAccuracy(confusion_matrix):
    counter = confusion_matrix.shape[0]
    list_diag = np.diag(confusion_matrix)
    list_raw_sum = np.sum(confusion_matrix, axis=1)
    each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))
    average_acc = np.mean(each_acc)
    return each_acc, average_acc


def reports (test_loader, y_test, name):
    count = 0
    # 模型测试
    for inputs, _ in test_loader:
        inputs = inputs.to(device)
        outputs = net(inputs)
        outputs = np.argmax(outputs.detach().cpu().numpy(), axis=1)
        if count == 0:
            y_pred =  outputs
            count = 1
        else:
            y_pred = np.concatenate( (y_pred, outputs) )

    if name == 'IP':
        target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn'
                        ,'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed', 
                        'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',
                        'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',
                        'Stone-Steel-Towers']
    elif name == 'SA':
        target_names = ['Brocoli_green_weeds_1','Brocoli_green_weeds_2','Fallow','Fallow_rough_plow','Fallow_smooth',
                        'Stubble','Celery','Grapes_untrained','Soil_vinyard_develop','Corn_senesced_green_weeds',
                        'Lettuce_romaine_4wk','Lettuce_romaine_5wk','Lettuce_romaine_6wk','Lettuce_romaine_7wk',
                        'Vinyard_untrained','Vinyard_vertical_trellis']
    elif name == 'PU':
        target_names = ['Asphalt','Meadows','Gravel','Trees', 'Painted metal sheets','Bare Soil','Bitumen',
                        'Self-Blocking Bricks','Shadows']
    
    classification = classification_report(y_test, y_pred, target_names=target_names)
    oa = accuracy_score(y_test, y_pred)
    confusion = confusion_matrix(y_test, y_pred)
    each_acc, aa = AA_andEachClassAccuracy(confusion)
    kappa = cohen_kappa_score(y_test, y_pred)
    
    return classification, confusion, oa*100, each_acc*100, aa*100, kappa*100

检测结果写在文件里:

classification, confusion, oa, each_acc, aa, kappa = reports(test_loader, ytest, 'IP')
classification = str(classification)
confusion = str(confusion)
file_name = "classification_report.txt"

with open(file_name, 'w') as x_file:
    x_file.write('\n')
    x_file.write('{} Kappa accuracy (%)'.format(kappa))
    x_file.write('\n')
    x_file.write('{} Overall accuracy (%)'.format(oa))
    x_file.write('\n')
    x_file.write('{} Average accuracy (%)'.format(aa))
    x_file.write('\n')
    x_file.write('\n')
    x_file.write('{}'.format(classification))
    x_file.write('\n')
    x_file.write('{}'.format(confusion))

下面代码用于显示分类结果:

# load the original image
X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']
y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']

height = y.shape[0]
width = y.shape[1]

X = applyPCA(X, numComponents= pca_components)
X = padWithZeros(X, patch_size//2)

# 逐像素预测类别
outputs = np.zeros((height,width))
for i in range(height):
    for j in range(width):
        if int(y[i,j]) == 0:
            continue
        else :
            image_patch = X[i:i+patch_size, j:j+patch_size, :]
            image_patch = image_patch.reshape(1,image_patch.shape[0],image_patch.shape[1], image_patch.shape[2], 1)
            X_test_image = torch.FloatTensor(image_patch.transpose(0, 4, 3, 1, 2)).to(device)                                   
            prediction = net(X_test_image)
            prediction = np.argmax(prediction.detach().cpu().numpy(), axis=1)
            outputs[i][j] = prediction+1
    if i % 20 == 0:
        print('... ... row ', i, ' handling ... ...')

存在的疑惑

标签:Exploring,DCNN,nn,HybridSN,self,patch,print,shape,test
来源: https://blog.csdn.net/qq_42505741/article/details/120594414