Mapping Rice Paddies in Complex Landscapes with Convolutional Neural Networks and Phenological Metrics

Figures & data

Figure 1. Location of the study area(112°17′-114°07′E, 26°03′-28°01′N).

Table 1. Acquisition times of the CCD images.

Number	Date	DOY1	Number	Date	DOY1
1 2 3 4 5 6 7 8	13/Jun 17/Jun 30/Jun 08/Jul 12/Jul 25/Jul 29/Jul 07/Aug	163 167 180 188 192 205 209 218	9 10 11 12 13 14 15 16	09/Aug 11/Aug 13/Aug 25/Aug 09/Sep 15/Sep 19/Sep 21/Sep	220 222 224 236 251 261 265 267

¹Note: DOY, day of year

Figure 2. Flowchart of the proposed methodology.

Figure 3. Original images, data pre-processing and feature extraction by LeNet-5.

Figure 4. Acquiring the phenological metrics of a paddy rice field.

Figure 5. Optimizing the model parameters. Plotted are the model accuracy versus time characteristics of the CNN for different (a) kernel sizes, (b) learning rates, (c) batch sizes and (d) activity functions.

Table 2. Accuracy assessment of the CNN model parameters.

	Kernel Size			Learning Rate				Batch Size		Activation Function
Parameter	3	4	5	0.05	0.1	0.2	0.5	50	100	ReLU	Sig^1
Accuracy	96%	98%	97%	98%	99%	99%	99%	98%	96%	99%	98%

¹Note: sig, sig is short for sigmoid

Table 3. Classification accuracy of (a) BPNN, (b) Original CNN, and (c) the pre-trained CNN.

	BPNN			Original CNN			Pre-trained CNN
Class	UA^1	PA^1	$\pm$2S^1	UA^1	PA^1	$\pm$2S^1	UA^1	PA^1	$\pm$2S^1
Cropland	0.53	0.11	0.01	0.43	0.33	0.02	0.82	0.62	0.01
Forest	0.50	0.88	0.04	0.58	0.77	0.04	0.89	0.97	0.03
Built-up	0.49	0.86	0.01	0.47	0.11	0.00	1.00	1.00	0.00
Water	0.96	0.83	0.00	0.86	0.96	0.00	1.00	0.89	0.00
OA	52.90%		0.02	55.15%		0.02	89.86%		0.02

¹Note: UA is short for user’s accuracy; PA is short for producer’s accuracy; OA is overall accuracy; $\pm$2S is the 95% confidence interval for accuracy; S is the estimated standard error of the estimated area proportion.

Figure 6. Classification results in Area A: (a) BPNN, (b) original CNN, (c) the pre-trained CNN and (d) the proposed coupling method. Classification results in Area B: (e) the pre-trained CNN and (f) the proposed coupling method. PRF: paddy rice field, AL: abandoned land.

Table 4. Classification accuracy of the proposed coupling method.

Class	Water	Built-up	Forest	PRF	AL	Map^1	$W_i$^1	UA	PA	$\pm$2S^1
Water	1383	0	0	0	0	12,270	0.05	1.00	0.72	0.00
Built-up	0	737	0	0	0	30,407	0.13	1.00	1.00	0.00
Forest	12	0	550	22	0	161,455	0.68	0.94	0.99	0.02
PRF	28	0	24	438	59	21,300	0.09	0.80	0.64	0.00
AL	11	0	0	98	301	13,758	0.06	0.73	0.82	0.00
OA	93.56%

¹Note: PRF is short for paddy rice field, AL is short for abandoned land, map is the estimated area of each class, $W_i$ is the portion of the area mapped as category i,UA is short for user’s accuracy; PA is short for producer’s accuracy; OA is overall accuracy; $\pm$2S is the 95% confidence interval for accuracy; S is the estimated standard error of the estimated area proportion.